http://qmrawiki.canr.msu.edu/api.php?action=feedcontributions&user=Yh&feedformat=atomQMRAwiki - User contributions [en]2022-05-29T06:18:12ZUser contributionsMediaWiki 1.28.2http://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2801Dose response assessment2011-11-16T22:49:00Z<p>Yh: /* Available Dose Response Models */</p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r) <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''<sup>*</sup>||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods, (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
| Beta-Poisson, α = 0.14, N<sub>50</sub> = 890.38 , ID<sub>50</sub> = 890.38, Human, A3249 strain, oral (in milk), Infection<br />
| Black et al., 1988; Medema et al., 1996 <br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
| Beta-Poisson, α = 0.36, N<sub>50</sub> = 4.93E+08, LD<sub>50</sub> = 4.93E+08, mice/ phase I Ohio strain, interperitoneal, Death <br />
| Williams et al., (1982) <br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
| Beta-Poisson, α = 0.16, N<sub>50</sub> = 2.11E+06, ID<sub>50</sub> = 2.11E+06, Human, EIEC 1624, oral with milk, Infection<br />
| DuPont et al., 1971<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
| Exponential, k = 2.18E-04, ID<sub>50</sub> = 3.18E+03, 3 month old pigs, EHEC O157:H7, strain 86-24, oral (in food), Infection<br />
| Cornick and Helgerson, 2004<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
| Beta-Poisson , α = 0.14, N<sub>50</sub> = 50.13, LD<sub>50</sub> = 50.13, Rhesus Macaques, Aerosol, Death<br />
| Saslaw and Carlisle (1966) <br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
| Beta-Poisson, α = 0.27, N<sub>50</sub> = 1.48E+03, ID<sub>50</sub> = 1.48E+03, Human, S. flexneri 2a (strain 2457T), oral with milk, Infection<br />
| DuPont et al. 1972<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|Beta-Poisson, α = 0.250, N<sub>50</sub> = 243, ID<sub>50</sub> = 243, Human, Inaba 569B strain, oral with NaHCO<sub>3</sub>, Infection<br />
|Hornick et al. 1971 <br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
| Exponential, k = 0.61, ID<sub>50</sub> = 1.14, Humans/type 4, Inhalation, Infection<br />
| Couch et al., 1966 <br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
| Beta-Poisson, α = 1.06, N<sub>50</sub> = 921.94, LD<sub>50</sub> = 921.94, humans/ echovirus-12 strain, oral, Infection<br />
| Schiff et al., (1984)<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
| Exponential, k = 3.75E-03, ID<sub>50</sub> = 185.10, pigs/ Porcine enterovirus type 7, oral, Infection<br />
| Cliver, (1981) <br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
| Beta-Poisson, α = 0.58, N<sub>50</sub> = 9.45E+05, ID<sub>50</sub> = 9.45E+05, Human, intranasal, Infection<br />
| Murphy et al., (1984), Murphy et al., (1985) <br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
| Exponential, k = 2.95, LD<sub>50</sub> = 0.24, guinea pig/ Josiah strain, Subcutaneous, Death<br />
| Jahrling et al., (1982)<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
| Beta-Poisson, α = 0.10, N<sub>50</sub> = 340.78, ID<sub>50</sub> = 340.78, Humans, Ingestion, Infection<br />
| Rendtorff, (1954) <br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
| Beta-Poisson, α = 1.76 N<sub>50</sub> = 1.04E+05, LD<sub>50</sub> = 1.04E+05, hamsters/scrapie strain 263K, oral, Death<br />
| Diringer et al., (1998)<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
==Criteria for choosing dose response models==<br />
<br />
We prefer dose response models with the following criteria, in rough order of importance: <br />
<br />
#Statistically acceptable fit (fail to reject goodness of fit, p > 0.05)<br />
#Human subjects, or animal models that mimic human pathophysiology well<br />
#Infection as the response, rather than disease, symptoms, or death<br />
#Exposure route similar/identical to the exposure route of natural infection<br />
#Pathogen strain is similar to strains causing natural infection<br />
#Pooled model using data from 2 or more experiments, provided the data sets are statistically similar (fail to reject that datasets are from the same distribution, p > 0.05)<br />
#Low ID<sub>50</sub>/LD<sub>50</sub> (to obtain a conservative risk estimate)<br />
<br />
We generally recommend a single dose response model, and we justify the decision in terms of the above criteria. This decision is somewhat subjective, since dose response datasets seldom meet all of these criteria. If all available models are unsatisfactory, we choose a single model to ‘recommend with reservations’. Our recommended model will seldom (if ever) be the best model for all applications. The user should carefully choose the model that is most appropriate for their particular problem.<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2800Dose response assessment2011-11-16T22:47:55Z<p>Yh: /* Available Dose Response Models */</p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r) <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''<sup>*</sup>||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods, (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
| Beta-Poisson, α = 0.14, N<sub>50</sub> = 890.38 , ID<sub>50</sub> = 890.38, Human, A3249 strain, oral (in milk), Infection<br />
| Black et al., 1988; Medema et al., 1996 <br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
| Beta-Poisson, α = 0.36, N<sub>50</sub> = 4.93E+08, LD<sub>50</sub> = 4.93E+08, mice/ phase I Ohio strain, interperitoneal, Death <br />
| Williams et al., (1982) <br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
| Beta-Poisson, α = 0.16, N<sub>50</sub> = 2.11E+06, ID<sub>50</sub> = 2.11E+06, Human, EIEC 1624, oral with milk, Infection<br />
| DuPont et al., 1971<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
| Exponential, k = 2.18E-04, ID<sub>50</sub> = 3.18E+03, 3 month old pigs, EHEC O157:H7, strain 86-24, oral (in food), Infection<br />
| Cornick and Helgerson, 2004<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
| Beta-Poisson , α = 0.14, N<sub>50</sub> = 50.13, LD<sub>50</sub> = 50.13, Rhesus Macaques, Aerosol, Death<br />
| Saslaw and Carlisle (1966) <br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
| Beta-Poisson, α = 0.27, N<sub>50</sub> = 1.48E+03, ID<sub>50</sub> = 1.48E+03, Human, S. flexneri 2a (strain 2457T), oral with milk, Infection<br />
| DuPont et al. 1972<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|Beta-Poisson, α = 0.250, N<sub>50</sub> = 243, ID<sub>50</sub> = 243, Human, Inaba 569B strain, oral with NaHCO3, Infection<br />
|Hornick et al. 1971 <br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
| Exponential, k = 0.61, ID<sub>50</sub> = 1.14, Humans/type 4, Inhalation, Infection<br />
| Couch et al., 1966 <br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
| Beta-Poisson, α = 1.06, N<sub>50</sub> = 921.94, LD<sub>50</sub> = 921.94, humans/ echovirus-12 strain, oral, Infection<br />
| Schiff et al., (1984)<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
| Exponential, k = 3.75E-03, ID<sub>50</sub> = 185.10, pigs/ Porcine enterovirus type 7, oral, Infection<br />
| Cliver, (1981) <br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
| Beta-Poisson, α = 0.58, N<sub>50</sub> = 9.45E+05, ID<sub>50</sub> = 9.45E+05, Human, intranasal, Infection<br />
| Murphy et al., (1984), Murphy et al., (1985) <br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
| Exponential, k = 2.95, LD<sub>50</sub> = 0.24, guinea pig/ Josiah strain, Subcutaneous, Death<br />
| Jahrling et al., (1982)<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
| Beta-Poisson, α = 0.10, N<sub>50</sub> = 340.78, ID<sub>50</sub> = 340.78, Humans, Ingestion, Infection<br />
| Rendtorff, (1954) <br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
| Beta-Poisson, α = 1.76 N<sub>50</sub> = 1.04E+05, LD<sub>50</sub> = 1.04E+05, hamsters/scrapie strain 263K, oral, Death<br />
| Diringer et al., (1998)<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
==Criteria for choosing dose response models==<br />
<br />
We prefer dose response models with the following criteria, in rough order of importance: <br />
<br />
#Statistically acceptable fit (fail to reject goodness of fit, p > 0.05)<br />
#Human subjects, or animal models that mimic human pathophysiology well<br />
#Infection as the response, rather than disease, symptoms, or death<br />
#Exposure route similar/identical to the exposure route of natural infection<br />
#Pathogen strain is similar to strains causing natural infection<br />
#Pooled model using data from 2 or more experiments, provided the data sets are statistically similar (fail to reject that datasets are from the same distribution, p > 0.05)<br />
#Low ID<sub>50</sub>/LD<sub>50</sub> (to obtain a conservative risk estimate)<br />
<br />
We generally recommend a single dose response model, and we justify the decision in terms of the above criteria. This decision is somewhat subjective, since dose response datasets seldom meet all of these criteria. If all available models are unsatisfactory, we choose a single model to ‘recommend with reservations’. Our recommended model will seldom (if ever) be the best model for all applications. The user should carefully choose the model that is most appropriate for their particular problem.<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2799Dose response assessment2011-11-16T22:46:04Z<p>Yh: /* Available Dose Response Models */</p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r) <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''<sup>*</sup>||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods, (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
| Beta-Poisson, α = 0.14, N<sub>50</sub> = 890.38 , ID<sub>50</sub> = 890.38, Human, A3249 strain, oral (in milk), Infection<br />
| Black et al., 1988; Medema et al., 1996 <br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
| Beta-Poisson, α = 0.36, N<sub>50</sub> = 4.93E+08, LD<sub>50</sub> = 4.93E+08, mice/ phase I Ohio strain, interperitoneal, Death <br />
| Williams et al., (1982) <br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
| Beta-Poisson, α = 0.16, N<sub>50</sub> = 2.11E+06, ID<sub>50</sub> = 2.11E+06, Human, EIEC 1624, oral with milk, Infection<br />
| DuPont et al., 1971<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
| Exponential, k = 2.18E-04, ID<sub>50</sub> = 3.18E+03, 3 month old pigs, EHEC O157:H7, strain 86-24, oral (in food), Infection<br />
| Cornick and Helgerson, 2004<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
| Beta-Poisson , α = 0.14, N<sub>50</sub> = 50.13, LD<sub>50</sub> = 50.13, Rhesus Macaques, Aerosol, Death<br />
| Saslaw and Carlisle (1966) <br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
| Beta-Poisson, α = 0.27, N<sub>50</sub> = 1.48E+03, ID<sub>50</sub> = 1.48E+03, Human, S. flexneri 2a (strain 2457T), oral with milk, Infection<br />
| DuPont et al. 1972<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|Beta-Poisson, α = 0.250, N<sub>50</sub> = 243, ID<sub>50</sub> = 243, Human, S. flexneri 2a (strain 2457T), oral with milk, Infection<br />
|<br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
| Exponential, k = 0.61, ID<sub>50</sub> = 1.14, Humans/type 4, Inhalation, Infection<br />
| Couch et al., 1966 <br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
| Beta-Poisson, α = 1.06, N<sub>50</sub> = 921.94, LD<sub>50</sub> = 921.94, humans/ echovirus-12 strain, oral, Infection<br />
| Schiff et al., (1984)<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
| Exponential, k = 3.75E-03, ID<sub>50</sub> = 185.10, pigs/ Porcine enterovirus type 7, oral, Infection<br />
| Cliver, (1981) <br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
| Beta-Poisson, α = 0.58, N<sub>50</sub> = 9.45E+05, ID<sub>50</sub> = 9.45E+05, Human, intranasal, Infection<br />
| Murphy et al., (1984), Murphy et al., (1985) <br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
| Exponential, k = 2.95, LD<sub>50</sub> = 0.24, guinea pig/ Josiah strain, Subcutaneous, Death<br />
| Jahrling et al., (1982)<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
| Beta-Poisson, α = 0.10, N<sub>50</sub> = 340.78, ID<sub>50</sub> = 340.78, Humans, Ingestion, Infection<br />
| Rendtorff, (1954) <br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
| Beta-Poisson, α = 1.76 N<sub>50</sub> = 1.04E+05, LD<sub>50</sub> = 1.04E+05, hamsters/scrapie strain 263K, oral, Death<br />
| Diringer et al., (1998)<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
==Criteria for choosing dose response models==<br />
<br />
We prefer dose response models with the following criteria, in rough order of importance: <br />
<br />
#Statistically acceptable fit (fail to reject goodness of fit, p > 0.05)<br />
#Human subjects, or animal models that mimic human pathophysiology well<br />
#Infection as the response, rather than disease, symptoms, or death<br />
#Exposure route similar/identical to the exposure route of natural infection<br />
#Pathogen strain is similar to strains causing natural infection<br />
#Pooled model using data from 2 or more experiments, provided the data sets are statistically similar (fail to reject that datasets are from the same distribution, p > 0.05)<br />
#Low ID<sub>50</sub>/LD<sub>50</sub> (to obtain a conservative risk estimate)<br />
<br />
We generally recommend a single dose response model, and we justify the decision in terms of the above criteria. This decision is somewhat subjective, since dose response datasets seldom meet all of these criteria. If all available models are unsatisfactory, we choose a single model to ‘recommend with reservations’. Our recommended model will seldom (if ever) be the best model for all applications. The user should carefully choose the model that is most appropriate for their particular problem.<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=File:Beta_Poisson_model_curve.png&diff=2796File:Beta Poisson model curve.png2011-11-16T21:38:19Z<p>Yh: </p>
<hr />
<div></div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=File:Alpha_N50_plot.png&diff=2795File:Alpha N50 plot.png2011-11-16T21:37:34Z<p>Yh: uploaded a new version of &quot;File:Alpha N50 plot.png&quot;</p>
<hr />
<div></div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=File:Alpha_N50_plot.png&diff=2794File:Alpha N50 plot.png2011-11-16T21:35:41Z<p>Yh: </p>
<hr />
<div></div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2474Dose response assessment2011-10-10T20:30:06Z<p>Yh: /* Available Dose Response Models */</p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r) <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''<sup>*</sup>||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods, (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
| Beta-Poisson, α = 0.36, N<sub>50</sub> = 4.93E+08, LD<sub>50</sub> = 4.93E+08, mice/ phase I Ohio strain, interperitoneal, Death <br />
| Williams et al., (1982) <br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
| Beta-Poisson, α = 1.06, N<sub>50</sub> = 921.94, LD<sub>50</sub> = 921.94, humans/ echovirus-12 strain, oral, Infection<br />
| Schiff et al., (1984)<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
| Exponential, k = 3.75E-03, ID<sub>50</sub> = 185.10, pigs/ Porcine enterovirus type 7, oral, Infection<br />
| Cliver, (1981) <br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
| Beta-Poisson, α = 0.58, N<sub>50</sub> = 9.45E+05, ID<sub>50</sub> = 9.45E+05, Human, intranasal, Infection<br />
| Murphy et al., (1984), Murphy et al., (1985) <br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
| Exponential, k = 2.95, LD<sub>50</sub> = 0.24, guinea pig/ Josiah strain, Subcutaneous, Death<br />
| Jahrling et al., (1982)<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
| Beta-Poisson, α = 1.76 N<sub>50</sub> = 1.04E+05, LD<sub>50</sub> = 1.04E+05, hamsters/scrapie strain 263K, oral, Death<br />
| Diringer et al., (1998)<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
<sup>*</sup> Prioritizing recommended models based on the following criteria (in the order of descending importance):<br />
<br />
1. Whether or not the fit is statistically acceptable<br />
<br />
2. Human data<br />
<br />
3. Animal model that best mimic human pathophysiology<br />
<br />
4. Infection as endpoint<br />
<br />
5. Natural exposure route<br />
<br />
6. Highly infective exposure route<br />
<br />
7. Common virulent strains<br />
<br />
8. Pooled model<br />
<br />
9. Model with lowest ID<sub>50</sub>/LD<sub>50</sub><br />
<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2473Dose response assessment2011-10-10T20:29:26Z<p>Yh: /* Available Dose Response Models */</p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r) <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''<sup>*</sup>||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods, (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
| Beta-Poisson, α = 0.36, N<sub>50</sub> = 4.93E+08, LD<sub>50</sub> = 4.93E+08, mice/ phase I Ohio strain, interperitoneal, Death <br />
| Williams et al., (1982) <br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
| Beta-Poisson, α = 1.06, N<sub>50</sub> = 921.94, LD<sub>50</sub> = 921.94, humans/ echovirus-12 strain, oral, Infection<br />
| Schiff et al., (1984)<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
| Exponential, k = 3.75E-03, ID<sub>50</sub> = 185.10, pigs/ Porcine enterovirus type 7, oral, Infection<br />
| Cliver, (1981) <br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
| Beta-Poisson, α = 0.58, N<sub>50</sub> = 9.45E+05, ID<sub>50</sub> = 9.45E+05, Human, intranasal, Infection<br />
| Murphy et al., (1984), Murphy et al., (1985) <br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
| Exponential, k = 2.95, LD<sub>50</sub> = 0.24, guinea pig/ Josiah strain, Subcutaneous, Death<br />
| Jahrling et al., (1982)<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
| Beta-Poisson, α = 1.76 N<sub>50</sub> = 1.04E+05, LD<sub>50</sub> = 1.04E+05, hamsters/scrapie strain 263K, oral, Death<br />
| Diringer et al., (1998)<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
<sup>*</sup> Prioritizing recommended models based on the following criteria (in the order of descending importance):<br />
<br />
1. Whether or not the fit is statistically acceptable<br />
<br />
2. Human data<br />
<br />
3. Animal model that best mimic human pathophysiology<br />
<br />
4. Infection as endpoint<br />
<br />
5. Natural exposure route<br />
<br />
6. Highly infective exposure route<br />
<br />
7. Common virulent strains<br />
<br />
8. Pooled model<br />
<br />
9. Model with lowest ID<sub>50</sub>/LD<sub>50</sub> if all above criteria are same<br />
<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2472Dose response assessment2011-10-10T20:27:57Z<p>Yh: </p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r) <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''<sup>*</sup>||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods, (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
| Beta-Poisson, α = 0.36, N<sub>50</sub> = 4.93E+08, LD<sub>50</sub> = 4.93E+08, mice/ phase I Ohio strain, interperitoneal, Death <br />
| Williams et al., (1982) <br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
| Beta-Poisson, α = 1.06, N<sub>50</sub> = 921.94, LD<sub>50</sub> = 921.94, humans/ echovirus-12 strain, oral, Infection<br />
| Schiff et al., (1984)<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
| Exponential, k = 3.75E-03, ID<sub>50</sub> = 185.10, pigs/ Porcine enterovirus type 7, oral, Infection<br />
| Cliver, (1981) <br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
| Beta-Poisson, α = 0.58, N<sub>50</sub> = 9.45E+05, ID<sub>50</sub> = 9.45E+05, Human, intranasal, Infection<br />
| Murphy et al., (1984), Murphy et al., (1985) <br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
| Exponential, k = 2.95, LD<sub>50</sub> = 0.24, guinea pig/ Josiah strain, Subcutaneous, Death<br />
| Jahrling et al., (1982)<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
| Beta-Poisson, α = 1.76 N<sub>50</sub> = 1.04E+05, LD<sub>50</sub> = 1.04E+05, hamsters/scrapie strain 263K, oral, Death<br />
| Diringer et al., (1998)<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
<sup>*</sup> Prioritizing recommended models based on the following criteria (in the order of descending importance):<br />
<br />
1. Whether or not the fit is statistically acceptable<br />
<br />
2. Human data<br />
<br />
3. Animal model that best mimic human pathophysiology<br />
<br />
4. Infection as endpoint<br />
<br />
5. Natural exposure route<br />
<br />
6. Highly infective exposure route<br />
<br />
7. Common virulent strains<br />
<br />
8. Pooled model<br />
<br />
9. Model with lowest ID50/LD50 if all above criteria are same<br />
<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2471Dose response assessment2011-10-10T20:26:37Z<p>Yh: /* Available Dose Response Models */</p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r) <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods, (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
| Beta-Poisson, α = 0.36, N<sub>50</sub> = 4.93E+08, LD<sub>50</sub> = 4.93E+08, mice/ phase I Ohio strain, interperitoneal, Death <br />
| Williams et al., (1982) <br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
| Beta-Poisson, α = 1.06, N<sub>50</sub> = 921.94, LD<sub>50</sub> = 921.94, humans/ echovirus-12 strain, oral, Infection<br />
| Schiff et al., (1984)<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
| Exponential, k = 3.75E-03, ID<sub>50</sub> = 185.10, pigs/ Porcine enterovirus type 7, oral, Infection<br />
| Cliver, (1981) <br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
| Beta-Poisson, α = 0.58, N<sub>50</sub> = 9.45E+05, ID<sub>50</sub> = 9.45E+05, Human, intranasal, Infection<br />
| Murphy et al., (1984), Murphy et al., (1985) <br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
| Exponential, k = 2.95, LD<sub>50</sub> = 0.24, guinea pig/ Josiah strain, Subcutaneous, Death<br />
| Jahrling et al., (1982)<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
| Beta-Poisson, α = 1.76 N<sub>50</sub> = 1.04E+05, LD<sub>50</sub> = 1.04E+05, hamsters/scrapie strain 263K, oral, Death<br />
| Diringer et al., (1998)<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
<sup>a</sup> Prioritizing recommended models based on the following criteria (in the order of descending importance):<br />
<br />
1. Whether or not the fit is statistically acceptable<br />
<br />
2. Human data<br />
<br />
3. Animal model that best mimic human pathophysiology<br />
<br />
4. Infection as endpoint<br />
<br />
5. Natural exposure route<br />
<br />
6. Highly infective exposure route<br />
<br />
7. Common virulent strains<br />
<br />
8. Pooled model<br />
<br />
9. Model with lowest ID50/LD50 if all above criteria are same<br />
<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2442Old Main Page2011-10-10T13:36:40Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the Quantitative Microbial Risk Assessment ([[Quantitative Microbial Risk Assessment|QMRA]]) field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
<br />
== QMRA Evolution and Development ==<br />
<br />
Quantitative microbial risk assessment (QMRA) is a framework and approach that brings information and data together with mathematical models to address the spread of microbial agents through environmental exposures and to characterize the nature of the adverse outcomes. Ultimately the goal in assessing risks is to develop and implement strategies that can monitor and control the risks (or safety) and allows one to respond to emerging diseases, outbreaks and emergencies that impact the safety of water, food, air, fomites and in general our outdoor and indoor environments. <br />
<br />
Risk by it’s nature is probabilistic and thus relies developing quantitative information.<br />
<br />
* The definition of RISK: chance*hazard*exposure*consequence<br />
* Risk is the likelihood of identified hazards causing harm in exposed populations in a specified time frame including the severity of the consequences.<br />
<br />
<br />
The traditional risk assessment process was seen as a four step process, Fig. 1 (NRC, Red Book, 1983) and was adopted for microbial hazards (Haas, Rose, Gerba, 1991). <br />
<br />
[[File:NAS_Paradigm.png|thumb|center|300px|Risk Paradigm from National Academies of Science]]<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism.<br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
<br />
<br />
More recently, according to the NRC’s ''Science and Decisions: Advancing Risk Assessment'' (NRC, 2008) the risk assessment-risk management paradigm should be integrated and include several phases.<br />
<br />
[[File:Three_Phases.png|center|Three phases for risk assessment-risk management paradigm]] <br />
<br />
The current QMRA framework shown in Figure 2 addresses the assessment-management integration and the need for monitoring to improve exposure assessment, characterization and management.<br />
<br />
'''Integration of Risk Assessment, Communication and Management'''<br />
<br />
*Understanding Best Use of Risk Assessment in Decision Making<br />
<br />
*Assessment-Analysis Advances<br />
<br />
*Infectious Disease Environmental Transmission Models<br />
<br />
*Modeling How Communities Communicate<br />
<br />
[[File:Advanced_Risk_Framework.png|thumb|center|300px|Advanced risk assessment-risk management framework]]<br />
<br />
<br />
<br />
<br />
== References ==<br />
<br />
Haas, C.N., J.B.Rose and C.P., Gerba. 1999. Quantitative Microbial Risk Assessment.1<sup>st</sup>.Ed. John Wiley & Sons, Inc., New York.<br />
<br />
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington DC. National Academy Press<br />
<br />
NRC (National Research Council) 2008. Science and Decisions: Advancing Risk Assessment. Washington DC. National Academy Press<br />
<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2441Old Main Page2011-10-10T13:34:58Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the Quantitative Microbial Risk Assessment ([[Quantitative Microbial Risk Assessment|QMRA]]) field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
<br />
== QMRA Evolution and Development ==<br />
<br />
Quantitative microbial risk assessment (QMRA) is a framework and approach that brings information and data together with mathematical models to address the spread of microbial agents through environmental exposures and to characterize the nature of the adverse outcomes. Ultimately the goal in assessing risks is to develop and implement strategies that can monitor and control the risks (or safety) and allows one to respond to emerging diseases, outbreaks and emergencies that impact the safety of water, food, air, fomites and in general our outdoor and indoor environments. <br />
<br />
Risk by it’s nature is probabilistic and thus relies developing quantitative information.<br />
<br />
* The definition of RISK: chance*hazard*exposure*consequence<br />
* Risk is the likelihood of identified hazards causing harm in exposed populations in a specified time frame including the severity of the consequences.<br />
<br />
<br />
The traditional risk assessment process was seen as a four step process, Fig. 1 (NRC, Red Book, 1983) and was adopted for microbial hazards (Haas, Rose, Gerba, 1991). <br />
<br />
[[File:NAS_Paradigm.png|thumb|center|300px|Risk Paradigm from National Academies of Science]]<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism.<br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
<br />
<br />
<br />
More recently, according to the NRC’s ''Science and Decisions: Advancing Risk Assessment'' (NRC, 2008) the risk assessment-risk management paradigm should be integrated and include several phases.<br />
<br />
[[File:Three_Phases.png|center|Three phases for risk assessment-risk management paradigm]] <br />
<br />
The current QMRA framework shown in Figure 2 addresses the assessment-management integration and the need for monitoring to improve exposure assessment, characterization and management.<br />
<br />
'''Integration of Risk Assessment, Communication and Management'''<br />
<br />
*Understanding Best Use of Risk Assessment in Decision Making<br />
<br />
*Assessment-Analysis Advances<br />
<br />
*Infectious Disease Environmental Transmission Models<br />
<br />
*Modeling How Communities Communicate<br />
<br />
[[File:Advanced_Risk_Framework.png|thumb|center|300px|Advanced risk assessment-risk management framework]]<br />
<br />
<br />
<br />
<br />
== References ==<br />
<br />
Haas, C.N., J.B.Rose and C.P., Gerba. 1999. Quantitative Microbial Risk Assessment.1<sup>st</sup>.Ed. John Wiley & Sons, Inc., New York.<br />
<br />
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington DC. National Academy Press<br />
<br />
NRC (National Research Council) 2008. Science and Decisions: Advancing Risk Assessment. Washington DC. National Academy Press<br />
<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2440Old Main Page2011-10-10T13:34:13Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the Quantitative Microbial Risk Assessment ([[Quantitative Microbial Risk Assessment|QMRA]]) field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
<br />
== QMRA Evolution and Development ==<br />
<br />
Quantitative microbial risk assessment (QMRA) is a framework and approach that brings information and data together with mathematical models to address the spread of microbial agents through environmental exposures and to characterize the nature of the adverse outcomes. Ultimately the goal in assessing risks is to develop and implement strategies that can monitor and control the risks (or safety) and allows one to respond to emerging diseases, outbreaks and emergencies that impact the safety of water, food, air, fomites and in general our outdoor and indoor environments. <br />
<br />
Risk by it’s nature is probabilistic and thus relies developing quantitative information.<br />
<br />
* The definition of RISK: chance*hazard*exposure*consequence<br />
* Risk is the likelihood of identified hazards causing harm in exposed populations in a specified time frame including the severity of the consequences.<br />
<br />
<br />
The traditional risk assessment process was seen as a four step process, Fig. 1 (NRC, Red Book, 1983) and was adopted for microbial hazards (Haas, Rose, Gerba, 1991). <br />
<br />
[[File:NAS_Paradigm.png|thumb|center|300px|Risk Paradigm from National Academies of Science]]<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism.<br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
<br />
<br />
<br />
More recently, according to the NRC’s ''Science and Decisions: Advancing Risk Assessment'' (NRC, 2008) the risk assessment-risk management paradigm should be integrated and include several phases.<br />
<br />
[[File:Three_Phases.png|center|Three phases for risk assessment-risk management paradigm]] <br />
<br />
The current QMRA framework shown in Figure 2 addresses the assessment-management integration and the need for monitoring to improve exposure assessment, characterization and management.<br />
<br />
'''Integration of Risk Assessment, Management and Communication'''<br />
<br />
*Understanding Best Use of Risk Assessment in Decision Making<br />
<br />
*Assessment-Analysis Advances<br />
<br />
*Infectious Disease Environmental Transmission Models<br />
<br />
*Modeling How Communities Communicate<br />
<br />
[[File:Advanced_Risk_Framework.png|thumb|center|300px|Advanced risk assessment-risk management framework]]<br />
<br />
<br />
<br />
<br />
== References ==<br />
<br />
Haas, C.N., J.B.Rose and C.P., Gerba. 1999. Quantitative Microbial Risk Assessment.1<sup>st</sup>.Ed. John Wiley & Sons, Inc., New York.<br />
<br />
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington DC. National Academy Press<br />
<br />
NRC (National Research Council) 2008. Science and Decisions: Advancing Risk Assessment. Washington DC. National Academy Press<br />
<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2439Old Main Page2011-10-10T13:29:10Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the Quantitative Microbial Risk Assessment ([[Quantitative Microbial Risk Assessment|QMRA]]) field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
<br />
== QMRA Evolution and Development ==<br />
<br />
Quantitative microbial risk assessment (QMRA) is a framework and approach that brings information and data together with mathematical models to address the spread of microbial agents through environmental exposures and to characterize the nature of the adverse outcomes. Ultimately the goal in assessing risks is to develop and implement strategies that can monitor and control the risks (or safety) and allows one to respond to emerging diseases, outbreaks and emergencies that impact the safety of water, food, air, fomites and in general our outdoor and indoor environments. <br />
<br />
Risk by it’s nature is probabilistic and thus relies developing quantitative information.<br />
<br />
* The definition of RISK: chance*hazard*exposure*consequence<br />
* Risk is the likelihood of identified hazards causing harm in exposed populations in a specified time frame including the severity of the consequences.<br />
<br />
<br />
The traditional risk assessment process was seen as a four step process, Fig. 1 (NRC, Red Book, 1983) and was adopted for microbial hazards (Haas, Rose, Gerba, 1991). <br />
<br />
[[File:NAS_Paradigm.png|thumb|center|300px|Risk Paradigm from National Academies of Science]]<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism.<br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
<br />
<br />
<br />
More recently, according to the NRC’s ''Science and Decisions: Advancing Risk Assessment'' (NRC, 2008) the risk assessment-risk management paradigm should be integrated and include several phases.<br />
<br />
[[File:Three_Phases.png|center|Three phases for risk assessment-risk management paradigm]] <br />
<br />
The current QMRA framework shown in Figure 2 addresses the assessment-management integration and the need for monitoring to improve exposure assessment, characterization and management.<br />
<br />
'''Risk Characterization, Management And Communication'''<br />
<br />
*Understanding Best Use of Risk Assessment in Decision Making<br />
<br />
*Assessment-Analysis Advances<br />
<br />
*Infectious Disease Environmental Transmission Models<br />
<br />
*Modeling How Communities Communicate<br />
<br />
[[File:Advanced_Risk_Framework.png|thumb|center|300px|Advanced risk assessment-risk management framework]]<br />
<br />
<br />
<br />
<br />
== References ==<br />
<br />
Haas, C.N., J.B.Rose and C.P., Gerba. 1999. Quantitative Microbial Risk Assessment.1<sup>st</sup>.Ed. John Wiley & Sons, Inc., New York.<br />
<br />
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington DC. National Academy Press<br />
<br />
NRC (National Research Council) 2008. Science and Decisions: Advancing Risk Assessment. Washington DC. National Academy Press<br />
<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2438Old Main Page2011-10-10T13:27:52Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the Quantitative Microbial Risk Assessment ([[Quantitative Microbial Risk Assessment|QMRA]]) field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
<br />
== QMRA Evolution and Development ==<br />
<br />
Quantitative microbial risk assessment (QMRA) is a framework and approach that brings information and data together with mathematical models to address the spread of microbial agents through environmental exposures and to characterize the nature of the adverse outcomes. Ultimately the goal in assessing risks is to develop and implement strategies that can monitor and control the risks (or safety) and allows one to respond to emerging diseases, outbreaks and emergencies that impact the safety of water, food, air, fomites and in general our outdoor and indoor environments. <br />
<br />
Risk by it’s nature is probabilistic and thus relies developing quantitative information.<br />
<br />
* The definition of RISK: chance*hazard*exposure*consequence<br />
* Risk is the likelihood of identified hazards causing harm in exposed populations in a specified time frame including the severity of the consequences.<br />
<br />
<br />
The traditional risk assessment process was seen as a four step process, Fig. 1 (NRC, Red Book, 1983) and was adopted for microbial hazards (Haas, Rose, Gerba, 1991). <br />
<br />
[[File:NAS_Paradigm.png|thumb|center|300px|Risk Paradigm from National Academies of Science]]<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism.<br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
<br />
<br />
<br />
More recently, according to the NRC’s ''Science and Decisions: Advancing Risk Assessment'' (NRC, 2008) the risk assessment-risk management paradigm should be integrated and include several phases.<br />
<br />
[[File:Three_Phases.png|center|Three phases for risk assessment-risk management paradigm]] <br />
<br />
The current QMRA framework shown in Figure 2 addresses the assessment-management integration and the need for monitoring to improve exposure assessment, characterization and management.<br />
<br />
[[File:Advanced_Risk_Framework.png|thumb|center|300px|Advanced risk assessment-risk management framework]]<br />
<br />
<br />
<br />
<br />
== References ==<br />
<br />
Haas, C.N., J.B.Rose and C.P., Gerba. 1999. Quantitative Microbial Risk Assessment.1<sup>st</sup>.Ed. John Wiley & Sons, Inc., New York.<br />
<br />
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington DC. National Academy Press<br />
<br />
NRC (National Research Council) 2008. Science and Decisions: Advancing Risk Assessment. Washington DC. National Academy Press<br />
<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2437Old Main Page2011-10-10T13:22:21Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the Quantitative Microbial Risk Assessment ([[Quantitative Microbial Risk Assessment|QMRA]]) field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
<br />
== QMRA Evolution and Development ==<br />
<br />
Quantitative microbial risk assessment (QMRA) is a framework and approach that brings information and data together with mathematical models to address the spread of microbial agents through environmental exposures and to characterize the nature of the adverse outcomes. Ultimately the goal in assessing risks is to develop and implement strategies that can monitor and control the risks (or safety) and allows one to respond to emerging diseases, outbreaks and emergencies that impact the safety of water, food, air, fomites and in general our outdoor and indoor environments. <br />
<br />
Risk by it’s nature is probabilistic and thus relies developing quantitative information.<br />
<br />
* The definition of RISK: chance*hazard*exposure*consequence<br />
* Risk is the likelihood of identified hazards causing harm in exposed populations in a specified time frame including the severity of the consequences.<br />
<br />
<br />
The traditional risk assessment process was seen as a four step process, Fig. 1 (NRC, Red Book, 1983) and was adopted for microbial hazards (Haas, Rose, Gerba, 1991). <br />
<br />
[[File:NAS_Paradigm.png|thumb|center|300px|Risk Paradigm from National Academies of Science]]<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
<br />
<br />
<br />
More recently, according to the NRC’s ''Science and Decisions: Advancing Risk Assessment'' (NRC, 2008) the risk assessment-risk management paradigm should be integrated and include several phases.<br />
<br />
[[File:Three_Phases.png|center|Three phases for risk assessment-risk management paradigm]] <br />
<br />
The current QMRA framework shown in Figure 2 addresses the assessment-management integration and the need for monitoring to improve exposure assessment, characterization and management.<br />
<br />
[[File:Advanced_Risk_Framework.png|thumb|center|300px|Advanced risk assessment-risk management framework]]<br />
<br />
<br />
'''Hazard Identification'''<br />
<br />
*Pathogen ID<br />
<br />
*Clinical Outcomes<br />
<br />
'''Exposure Assessment and Monitoring '''<br />
<br />
*Detection<br />
<br />
*Fate and Transport<br />
<br />
*Development of Surrogates for Field and Challenge Studies<br />
<br />
*Protocols for Use of Surrogates <br />
<br />
'''Dose Response Modeling'''<br />
<br />
*Classical Models<br />
<br />
*Advanced Dose Response Models<br />
<br />
*Mechanistic and Physiologically Based Models<br />
<br />
'''Risk Characterization, Management And Communication'''<br />
<br />
*Understanding Best Use of Risk Assessment in Decision Making<br />
<br />
*Assessment-Analysis Advances<br />
<br />
*Infectious Disease Environmental Transmission Models<br />
<br />
*Modeling How Communities Communicate<br />
<br />
<br />
<br />
== References ==<br />
<br />
Haas, C.N., J.B.Rose and C.P., Gerba. 1999. Quantitative Microbial Risk Assessment.1<sup>st</sup>.Ed. John Wiley & Sons, Inc., New York.<br />
<br />
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington DC. National Academy Press<br />
<br />
NRC (National Research Council) 2008. Science and Decisions: Advancing Risk Assessment. Washington DC. National Academy Press<br />
<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2436Old Main Page2011-10-10T13:21:30Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the Quantitative Microbial Risk Assessment ([[Quantitative Microbial Risk Assessment|QMRA]]) field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
Quantitative microbial risk assessment (QMRA) is a framework and approach that brings information and data together with mathematical models to address the spread of microbial agents through environmental exposures and to characterize the nature of the adverse outcomes. Ultimately the goal in assessing risks is to develop and implement strategies that can monitor and control the risks (or safety) and allows one to respond to emerging diseases, outbreaks and emergencies that impact the safety of water, food, air, fomites and in general our outdoor and indoor environments. <br />
<br />
Risk by it’s nature is probabilistic and thus relies developing quantitative information.<br />
<br />
* The definition of RISK: chance*hazard*exposure*consequence<br />
* Risk is the likelihood of identified hazards causing harm in exposed populations in a specified time frame including the severity of the consequences.<br />
<br />
<br />
The traditional risk assessment process was seen as a four step process, Fig. 1 (NRC, Red Book, 1983) and was adopted for microbial hazards (Haas, Rose, Gerba, 1991). <br />
<br />
[[File:NAS_Paradigm.png|thumb|center|300px|Risk Paradigm from National Academies of Science]]<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
<br />
<br />
<br />
More recently, according to the NRC’s ''Science and Decisions: Advancing Risk Assessment'' (NRC, 2008) the risk assessment-risk management paradigm should be integrated and include several phases.<br />
<br />
[[File:Three_Phases.png|center|Three phases for risk assessment-risk management paradigm]] <br />
<br />
The current QMRA framework shown in Figure 2 addresses the assessment-management integration and the need for monitoring to improve exposure assessment, characterization and management.<br />
<br />
[[File:Advanced_Risk_Framework.png|thumb|center|300px|Advanced risk assessment-risk management framework]]<br />
<br />
<br />
'''Hazard Identification'''<br />
<br />
*Pathogen ID<br />
<br />
*Clinical Outcomes<br />
<br />
'''Exposure Assessment and Monitoring '''<br />
<br />
*Detection<br />
<br />
*Fate and Transport<br />
<br />
*Development of Surrogates for Field and Challenge Studies<br />
<br />
*Protocols for Use of Surrogates <br />
<br />
'''Dose Response Modeling'''<br />
<br />
*Classical Models<br />
<br />
*Advanced Dose Response Models<br />
<br />
*Mechanistic and Physiologically Based Models<br />
<br />
'''Risk Characterization, Management And Communication'''<br />
<br />
*Understanding Best Use of Risk Assessment in Decision Making<br />
<br />
*Assessment-Analysis Advances<br />
<br />
*Infectious Disease Environmental Transmission Models<br />
<br />
*Modeling How Communities Communicate<br />
<br />
<br />
<br />
== References ==<br />
<br />
Haas, C.N., J.B.Rose and C.P., Gerba. 1999. Quantitative Microbial Risk Assessment.1<sup>st</sup>.Ed. John Wiley & Sons, Inc., New York.<br />
<br />
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington DC. National Academy Press<br />
<br />
NRC (National Research Council) 2008. Science and Decisions: Advancing Risk Assessment. Washington DC. National Academy Press<br />
<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2433Old Main Page2011-10-10T13:02:20Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the Quantitative Microbial Risk Assessment ([[Quantitative Microbial Risk Assessment|QMRA]]) field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The current paradigm for risk assessment was developed by the National Research Council (NRC) as part of the National Academy of Sciences (NAS) and recorded in a set of guidelines initially intended for federal agencies. The paradigm is an interdisciplinary approach, such as risk assessment and QMRA itself. QMRA follows both the thought processes as well as the steps in analysis which the risk assessment should follow, as shown in the bulleted list below:<br />
<br />
Quantitative microbial risk assessment (QMRA) is a framework and approach that brings information and data together with mathematical models to address the spread of microbial agents through environmental exposures and to characterize the nature of the adverse outcomes. Ultimately the goal in assessing risks is to develop and implement strategies that can monitor and control the risks (or safety) and allows one to respond to emerging diseases, outbreaks and emergencies that impact the safety of water, food, air, fomites and in general our outdoor and indoor environments. <br />
<br />
Risk by it’s nature is probabilistic and thus relies developing quantitative information.<br />
<br />
* The definition of RISK: chance*hazard*exposure*consequence<br />
* Risk is the likelihood of identified hazards causing harm in exposed populations in a specified time frame including the severity of the consequences.<br />
<br />
Quantitative Microbial Risk Assessment (QMRA) can be used to integrate medicine, biology, environmental processes, engineering and mathematics as well as decision science to address the vast array of microbial risks and infectious diseases now facing our communities and the world in the 21st century. <br />
<br />
The traditional risk assessment process was seen as a four step process, Fig. 1 (NRC, Red Book, 1983) and was adopted for microbial hazards (Haas, Rose, Gerba, 1991). <br />
<br />
[[File:NAS_Paradigm.png|thumb|center|300px|Risk Paradigm from National Academies of Science]]<br />
<br />
More recently, according to the NRC’s ''Science and Decisions: Advancing Risk Assessment'' (NRC, 2008) the risk assessment-risk management paradigm should be integrated and include several phases.<br />
<br />
[[File:Three_Phases.png|center|Three phases for risk assessment-risk management paradigm]] <br />
<br />
The current QMRA framework shown in Figure 2 addresses the assessment-management integration and the need for monitoring to improve exposure assessment, characterization and management.<br />
<br />
[[File:Advanced_Risk_Framework.png|thumb|center|300px|Advanced risk assessment-risk management framework]]<br />
<br />
<br />
'''Hazard Identification '''<br />
<br />
*Pathogen ID<br />
<br />
*Clinical Outcomes<br />
<br />
'''Exposure Assessment and Monitoring '''<br />
<br />
*Detection<br />
<br />
*Fate and Transport<br />
<br />
*Development of Surrogates for Field and Challenge Studies<br />
<br />
*Protocols for Use of Surrogates <br />
<br />
'''Dose Response Modeling'''<br />
<br />
*Classical Models<br />
<br />
*Advanced Dose Response Models<br />
<br />
*Mechanistic and Physiologically Based Models<br />
<br />
'''Risk Characterization, Management And Communication'''<br />
<br />
*Understanding Best Use of Risk Assessment in Decision Making<br />
<br />
*Assessment-Analysis Advances<br />
<br />
*Infectious Disease Environmental Transmission Models<br />
<br />
*Modeling How Communities Communicate<br />
<br />
<br />
<br />
== References ==<br />
<br />
Haas, C.N., J.B.Rose and C.P., Gerba. 1999. Quantitative Microbial Risk Assessment.1<sup>st</sup>.Ed. John Wiley & Sons, Inc., New York.<br />
<br />
NRC (National Research Council) 1983. Risk Assessment in the Federal Government: Managing the Process. Washington DC. National Academy Press<br />
<br />
NRC (National Research Council) 2008. Science and Decisions: Advancing Risk Assessment. Washington DC. National Academy Press<br />
<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Bacillus_anthracis&diff=2432Bacillus anthracis2011-10-10T12:41:46Z<p>Yh: categorization</p>
<hr />
<div>{{PSDS|Herbivorous mammals such as livestock, not typically humans|Cutaneous: skin contact with spores from infected animals. Gastrointestinal: eating poorly cooked meat/dairy from infected animal. Inhalation: Inhalation of spores|Rare for cutaneous, none for inhalation/gastrointestinal|Cutaneous: 0-1 day. Other forms: 1-7 days, rarely up to 60 days|Cutaneous with treatment 1%, without treatment 20%. Inhalation 75% despite treatment|A vaccine is available for persons at increased risk (e.g., lab workers, military). Cutaneous anthrax is readily treatable with various antibiotics|Gram +, aerobic, encapsulated, nonmotile, spore-forming, rod-shaped bacterium|Extremely hardy spores can persist for years, even decades|Exponential model: optimal value of k is 1.65E-05 <br />[[File:Exponential_model.png|thumb|center|200px]] }}<br />
<br />
<br><br />
[[Dose response models for Bacillus anthracis]]<br />
<br><br />
[[Category:PSDS]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Category:PSDS&diff=2431Category:PSDS2011-10-10T12:40:25Z<p>Yh: Added PSDS category page</p>
<hr />
<div>These pages summarize basic information about particular pathogens. These summaries are called Pathogen Safety Data Sheets (PSDSs), by analogy with the well-known Materials Safety Data Sheets (MSDSs).</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Campylobacter_jejuni&diff=2430Campylobacter jejuni2011-10-10T12:39:14Z<p>Yh: categorization</p>
<hr />
<div>{{PSDS|Animals and humans|Fecal-oral, ingestion of contaminated food or water, and the eating of raw meat| | |Does not commonly cause death, it has been estimated that approximately 124 persons with Campylobacter infections die each year (CDC)|Recover without any specific treatment. Patients should drink extra fluids as long as the diarrhea lasts. In more severe cases, antibiotics such as azithromycin or erythromycin can shorten the duration of symptoms if given early in the illness|Gram-negative, spiral, and microaerophilic. Motile, with either unipolar or bipolar flagella, the organisms have a characteristic spiral/corkscrew appearance and are oxidase-positive|Extremely hardy spores can persist for years, even decades|}}<br />
<br />
<br><br />
[[Dose response models for Campylobacter]]<br />
<br><br />
[[Category:PSDS]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Adenovirus&diff=2429Adenovirus2011-10-10T12:38:25Z<p>Yh: categorization</p>
<hr />
<div>{{PSDS|Animals and human |Direct contact, fecal-oral transmission, and occasionally waterborne transmission |High |3-10 days |Over 300 adenovirus infections in immunocompromised patients, with an overall case fatality rate of 48% (Hierholzer JC, 1992) |Most infections are mild and require no therapy or only symptomatic treatment. Because there is no virus-specific therapy, serious adenovirus illness can be managed only by treating symptoms and complications of the infection |Medium-sized (90-100 nm), nonenveloped icosohedral viruses containing double-stranded DNA |Unusually stable to chemical and physical agents and to adverse pH conditions| }}<br />
<br />
<br><br />
[[Dose response models for Adenovirus]]<br />
<br><br />
[[Category:PSDS]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2426Old Main Page2011-10-10T12:31:00Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date quantitative information and knowledge developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The current paradigm for risk assessment was developed by the National Research Council (NRC) as part of the National Academy of Sciences (NAS) and recorded in a set of guidelines initially intended for federal agencies. The paradigm is an interdisciplinary approach, such as risk assessment and QMRA itself. QMRA follows both the thought processes as well as the steps in analysis which the risk assessment should follow, as shown in the bulleted list below:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2425Old Main Page2011-10-10T12:17:26Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The current paradigm for risk assessment was developed by the National Research Council (NRC) as part of the National Academy of Sciences (NAS) and recorded in a set of guidelines initially intended for federal agencies. The paradigm is an interdisciplinary approach, such as risk assessment and QMRA itself. QMRA follows both the thought processes as well as the steps in analysis which the risk assessment should follow, as shown in the bulleted list below:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2424Old Main Page2011-10-10T12:15:16Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Our future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The current paradigm for risk assessment was developed by the National Research Council (NRC) as part of the National Academy of Sciences (NAS) and recorded in a set of guidelines initially intended for federal agencies. The paradigm is an interdisciplinary approach, such as risk assessment and QMRA itself. QMRA follows both the thought processes as well as the steps in analysis which the risk assessment should follow, as shown in the bulleted list below:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2423Old Main Page2011-10-10T03:47:10Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge which [[CAMRA]] has developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Out future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The current paradigm for risk assessment was developed by the National Research Council (NRC) as part of the National Academy of Sciences (NAS) and recorded in a set of guidelines initially intended for federal agencies. The paradigm is an interdisciplinary approach, such as risk assessment and QMRA itself. QMRA follows both the thought processes as well as the steps in analysis which the risk assessment should follow, as shown in the bulleted list below:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2422Old Main Page2011-10-10T03:36:33Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge which [[CAMRA]] has developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Out future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The current paradigm for risk assessment was developed by the National Research Council (NRC) as part of the National Academy of Sciences (NAS) and recorded in a set of guidelines initially intended for federal agencies (NAS, 1983). The paradigm is an interdisciplinary approach, such as risk assessment and QMRA is itself. It follows both the thought processes as well as the steps in analysis which the risk assessment should follow, as shown in the bulleted list below:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2421Old Main Page2011-10-10T03:19:03Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge which [[CAMRA]] has developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Out future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The current paradigm for risk assessment was developed by the National Research Council (NRC) as part of the National Academy of Sciences (NAS) and recorded in a set of guidelines initially intended for federal agencies (NAS, 1983). The paradigm is an interdisciplinary approach, such as risk assessment and QMRA is itself. It follows both the thought processes as well as the steps in analysis which the risk assessment should follow, as shown in the bulleted list below (NAS, 1983):<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2420Old Main Page2011-10-10T02:18:58Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge which [[CAMRA]] has developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Out future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The four key steps of quantitative microbial risk assessment (QMRA) are the following:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]: The risk communication is an interactive process of exchange of information and opinion on risk among risk assessors, risk managers, stakeholders and general public.<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2419Old Main Page2011-10-10T02:14:06Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge which [[CAMRA]] has developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Out future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The four key steps of quantitative microbial risk assessment (QMRA) are the following:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2418Old Main Page2011-10-10T02:06:14Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge which [[CAMRA]] has developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Out future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The four key areas of quantitative microbial risk assessment (QMRA) are the following:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
<br />
Some other important developments of QMRA includes:<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2417Old Main Page2011-10-10T02:01:52Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge which [[CAMRA]] has developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Out future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The four key areas of microbial risk assessment (MRA) are the following:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]: The dose response analysis provides a quantitative relationship between the likelihood of adverse effects and the level of microbial exposure. Without knowing how different levels of the stressor affect the individual a sizable portion of quantified risk estimate will not be possible. The dose response assessment phase is arguably the most important phase in the QMRA paradigm. <br />
*[[Exposure assessment]]: The exposure assessment identifies affected population, determines the exposure pathways and environmental fate and transport, calculates the amount, frequency, length of time of exposure, and estimates dose or distribution of doses for an exposure.<br />
*[[Risk characterization]]: The risk characterization Integrates dose-response analysis and exposure assessment to estimate the magnitude of risk, uncertainty and variability of the hazard.<br />
*[[QMRA Tools]]: A variety of QMRA tools begin as simplistic tools essentially established and tested Microsoft Excel spreadsheets. Some QMRA tools are being developed as standalone computer applications.<br />
*[[Environmental Infection Transmission Systems]] (EITS): This work advances the conceptual framework for the science of environmental mediation of person to person transmitted infections by stochastic processes.<br />
*[[Risk communication]]<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Old_Main_Page&diff=2416Old Main Page2011-10-10T01:59:25Z<p>Yh: </p>
<hr />
<div><big>'''Welcome to the CAMRA Microbial Risk Assessment Wiki (CAMRAwiki)'''</big><br />
<br />
This website is dedicated to delivering the most up-to-date information and knowledge which [[CAMRA]] has developed in the [[Quantitative Microbial Risk Assessment|QMRA]] field. Out future goal is to become a central repository for QMRA knowledge and data. It is our intent to be a peer reviewed wiki where if users or visitors wish to add to our wiki they are welcome to submit their proposed addition to [mailto:camra@msu.edu camra@msu.edu] for one of our experts to review for inclusion to the wiki.<br />
<br />
This site is intended to provide easy instruction about QMRAs as well as data to perform your own QMRAs. <br />
<br />
Links on the left allow you to access pathogen information that CAMRA has compiled. Each pathogen has a Pathogen Safety Data Sheet (PSDS) that gives a brief overview of the hazard and its associated risks.<br />
<br />
The four key areas of microbial risk assessment (MRA) are the following:<br />
<br />
*[[Hazard identification]]: The hazard identification is both identification of the microbial agent and the spectrum of human illness and disease associated with the specific microorganism. <br />
*[[Dose response assessment]]<br />
*[[Exposure assessment]]<br />
*[[Risk characterization]]<br />
*[[QMRA Tools]]<br />
*[[Environmental Infection Transmission Systems]] (EITS)<br />
*[[Risk communication]]<br />
<br />
Thank you for using CAMRAwiki.<br />
<br />
----<br />
<br />
This work was supported by EPA/DHS CAMRA Center Grant RD832362, but this wiki does not necessarily reflect the opinion of either the US Environmental Protection Agency or the US Department of Homeland Security.<br />
<br />
----<br />
*[[BA Practice Organization|Page for Joan to review]]<br />
*[[BA Practice|Working links]]</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Naegleria_fowleri:_Dose_Response_Models&diff=2356Naegleria fowleri: Dose Response Models2011-10-05T15:44:10Z<p>Yh: /* Summary Data */</p>
<hr />
<div>==<center>'''Naegleria'''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://wiki.camra.msu.edu/images/8/84/Naegleria.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview '''===<br />
<br />
Naegleria, an ameboflagellate, has three stages in its life cycle: trophozoite, cyst, and a temporary flagellate stage. ''Naegleria fowleri'', a human pathogen, is thermophilic, tolerating temperatures of 40<sup>O</sup>C-45<sup>O</sup>C, while ''Naegleria gruberi'' is nonpathogenic, with an optimal growth temperature of 22<sup>O</sup>C-35<sup>O</sup>C. Other known nonpathogenic species include ''Naegleria lovaniensis'', ''Naegleria jadini'', and ''Naegleria australiensis'', although ''Naegleria australiensis italica'' has been shown to be a highly pathogenic subspecies in experimental animals. ''Naegleria fowleri'' is highly pathogenic and death may follow within a few days after the symptom onset (Ma et al. 1990).<br />
<br />
Sources for Naegleria have been reported as water, soil, sewage sludge, cooling towers, nasal and throat swabs, hospital hydrothermal pools, and swimming pools. ''Naegleria fowleri'', the most pathogenic species, has been isolated frequently from thermally polluted waters and sewage wastes. Most human infections with ''Naegleria fowleri'' have been associated with swimming in warm waters, but also with the sources of tap water and hot baths (Ma et al. 1990).<br />
<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
Adams et al. (1976) challenged three groups of male DUB/ICR mice intravenously with graded doses of ''Naegleria fowleri'' LEE strain and the survival was monitored for two weeks.<br />
<br />
Haggerty and John (1978) inoculated male DUB/ICR mice with ''Naegleria fowleri'' LEE strain via intravenous route and monitored the survival for three weeks.<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ '''Table 1.1. Summary of the Naegleria data and best fits'''<br />
| '''Experiment number''' || '''Reference''' || '''Host type/pathogen strain''' || '''Route/number of doses''' || '''Dose units''' || '''Response''' || '''Best fit model''' || '''Best-fit parameters''' || '''LD<sub>50</sub>'''<br />
|-<br />
| 1 || Adams et al. 1976 || mice/Naegleria fowleri LEE strain || intravenous /3 || no. of organisms || Death || exponential || k = 4.21E-07 || 1.64E+06<br />
|-<br />
| 2 || Haggerty and John 1978 || mice/Naegleria fowleri LEE strain || intravenous/4 || no. of organisms || Death || exponential || k = 3.07E-07 || 2.26E+06<br />
|- style="background-color:#cccccc;border-top:none;border-bottom:none;border-left:none;border-right:none;padding:0.0201in;"<br />
| 1 and 2 * || - || - || - || - || - || exponential || k = 3.42E-07 || 2.03E+06<br />
|}<br />
|}<br />
<br />
'''The data from experiments 1 and 2 were able to be statistically pooled.'''<br />
<br />
<br />
----<br />
<br />
=== '''<sup>*</sup>Recommended Model''' ===<br />
<br />
It is recommended that the pooled experiments 1 and 2 should be used as the best dose-response model. Both strains are common in outbreaks. The pooling narrows the range of the confidence region of the parameter estimates and enhances the statistical precision.<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Adams1976_Naegleria_datafitconf_1.xls '''Table 1.2. mice/Naegleria fowleri LEE strain data''']<br />
| Dose|| Dead || Survived || Total<br />
|-<br />
| 2500000 || 4 || 6 || 10<br />
|-<br />
| 5000000 || 19 || 1 || 20<br />
|-<br />
| 10000000 || 10 || 0 || 10<br />
|-<br />
| colspan = "4" | Adams et al. 1976<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Adams1976_Naegleria_datafitconf_1.xls '''Table 1.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 4.11<br />
| rowspan = "2" | 5.00E-04<br />
| 2<br />
| rowspan = "2" | 3.84 <br /> ''0.998''<br />
| 5.99 <br /> ''0.128''<br />
|-<br />
| Beta Poisson<br />
| 4.11<br />
| 1<br />
| 3.84 <br /> ''0.0426''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Adams1976_Naegleria_datafitconf_1.xls '''Table 1.4 Optimized parameters for the best fitting (Exponential), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 4.21E-07<br />
| 2.77E-07<br />
| 3.04E-07<br />
| 3.25E-07<br />
| 5.95E-07<br />
| 6.22E-07<br />
| 6.79E-07<br />
|-<br />
| LD<sub>50</sub><br />
| 1.64E+06<br />
| 1.02E+06<br />
| 1.11E+06<br />
| 1.16E+06<br />
| 2.13E+06<br />
| 2.28E+06<br />
| 2.50E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Adams1976_Naegleria_ExpHist.png|thumb|left|500px|'''Figure 1.1 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Adams1976_Naegleria_ExpModel.png|thumb|none|500px|'''Figure 1.2 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 2'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:HaggertyandJohn1978_Naegleria_datafitconf_2.xls '''Table 1.5 Mice/Naegleria fowleri LEE strain data''']<br />
| Dose || Dead || Survived || Total<br />
|-<br />
| 1000000 || 4 || 16 || 20<br />
|-<br />
| 2500000 || 12 || 8 || 20<br />
|-<br />
| 5000000 || 14 || 6 || 20<br />
|-<br />
| 10000000 || 20 || 0 || 20<br />
|-<br />
| colspan = "4" | Haggerty and John 1978<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:HaggertyandJohn1978_Naegleria_datafitconf_2.xls '''Table 1.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 3.47<br />
| rowspan = "2" | 9.00E-04<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''0.976''<br />
| 7.81 <br /> ''0.325''<br />
|-<br />
| Beta Poisson<br />
| 3.47<br />
| 2<br />
| 5.99 <br /> ''0.177''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:HaggertyandJohn1978_Naegleria_datafitconf_2.xls '''Table 1.7 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 3.07E-07<br />
| 2.14E-07<br />
| 2.33E-07<br />
| 2.43E-07<br />
| 4.02E-07<br />
| 4.26E-07<br />
| 4.70E-07<br />
|-<br />
| LD<sub>50</sub> (spores)<br />
| 2.26E+06<br />
| 1.47E+06<br />
| 1.63E+06<br />
| 1.73E+06<br />
| 2.85E+06<br />
| 2.97E+06<br />
| 3.24E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Haggerty1978_Naegleria_ExpHist.png|thumb|left|500px|'''Figure 1.3. Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Haggerty1978_Naegleria_ExpModel.png|thumb|none|500px|'''Figure 1.4. Exponential model plot, confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 3'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Adams1976HaggertyandJohn1978_Naegleria_datafitconf_3.xls '''Table 1.8: mice/ Naegleria fowleri LEE strain model data''']<br />
| Dose|| Dead || Survived || Total<br />
|-<br />
| 2500000 || 4 || 6 || 10<br />
|-<br />
| 5000000 || 19 || 1 || 20<br />
|-<br />
| 10000000 || 10 || 0 || 10<br />
|-<br />
| 1000000 || 4 || 16 || 20<br />
|-<br />
| 2500000 || 12 || 8 || 20<br />
|-<br />
| 5000000 || 14 || 6 || 20<br />
|-<br />
| 10000000 || 20 || 0 || 20<br />
|-<br />
| colspan = "4" | Adams et al. 1976 & Haggerty and John 1978<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Adams1976HaggertyandJohn1978_Naegleria_datafitconf_3.xls '''Table 1.9: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 8.85<br />
| rowspan = "2" | 5.00E-04<br />
| 6<br />
| rowspan = "2" | 3.84 <br /> ''0.982''<br />
| 12.59 <br /> ''0.182''<br />
|-<br />
| Beta Poisson<br />
| 8.85<br />
| 5<br />
| 11.07 <br /> ''0.115''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Adams1976HaggertyandJohn1978_Naegleria_datafitconf_3.xls '''Table 1.10 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 3.42E-07<br />
| 2.59E-07<br />
| 2.76E-07<br />
| 2.86E-07<br />
| 4.15E-07<br />
| 4.31E-07<br />
| 4.65E-07<br />
|-<br />
| LD<sub>50</sub> (spores)<br />
| 2.03E+06<br />
| 1.49E+06<br />
| 1.61E+06<br />
| 1.67E+06<br />
| 2.42E+06<br />
| 2.51E+06<br />
| 2.67E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Pooling_Naegleria_ExpHist.png|thumb|left|500px|'''Figure 1.5. Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Pooling_Naegleria_ExpModel.png|thumb|none|500px|'''Figure 1.6. Exponential model plot, confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
By increasing the number of data points, the pooling narrows the range of the confidence region of the parameter estimates and enhances the statistical precision.<br />
<br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
Adams, A.C., John, D.T. and Bradley, S.G. (1976) [http://iai.asm.org/cgi/reprint/13/5/1387 Modification of resistance of mice to naegleria fowleri infections]. ''Infection and Immunity'' '''13''', 1387-1391.<br />
<br />
Haggerty, R.M. and John, D.T. (1978) [http://iai.asm.org/cgi/reprint/20/1/73 Innate resistance of mice to experimental infection with naegleria fowleri]. ''Infection and Immunity'' '''20''', 73-77.<br />
<br />
Ma, P., Visvesvara, G.S., Martinez, A.J., Theodore, F.H., Daggett, P.M. and Sawyer, T.K. (1990) [http://www.jstor.org/stable/4455558 Naegleria and acanthamoeba infections: Review]. ''Reviews of Infectious Diseases'' '''12''', 490-513.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=SARS:_Dose_Response_Models&diff=2355SARS: Dose Response Models2011-10-05T15:42:47Z<p>Yh: /* Summary Data */</p>
<hr />
<div>==<center>'''SARS'''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://wiki.camra.msu.edu/images/f/ff/SARS.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview '''===<br />
<br />
Coronaviruses cause acute and chronic respiratory, enteric, and central nervous system (CNS) diseases in humans and many species of animals. Coronaviruses are divided into three groups based on the genome sequences, including SARS-CoV (a member of group II) as well as murine hepatitis virus (MHV), bovine coronavirus, porcine hemagglutinating encephalomyelitis virus (HEV), equine coronavirus, and human coronavirues OC43 and NL63, which also cause respiratory infections. SARS-CoV, the causal pathogen of severe acute respiratory syndrome (SARS), caused a large outbreak of this severe pneumonia occurred in Hong Kong in 2003 and rapidly spread throughout the world. SARS-CoV can infect and replicate in mice, ferrets, hamsters, cats, and several species of nonhuman primates (cynomolgus and rhesus macaques, African green monkeys, and marmosets). MHV that infects both mice and rats often has been studied as a suitable model of human coronavirus diseases (Watanabe et al. 2010). <br />
<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
DeDiego et al. (2008) challenged four groups of the tg mice intranasally with graded doses of rSARS-CoV and the survival was monitored for 13 days.<br />
<br />
De Albuquerque et al. (2006) inoculated A/J mice with MHV-1 intranasally via intranasal route and monitored the survival for 21 days.<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ '''Table 8.1. Summary of the SARS data and best fits'''<br />
| '''Experiment number''' || '''Reference''' || '''Host type/pathogen strain''' || '''Route/number of doses''' || '''Dose units''' || '''Response''' || '''Best-fit model''' || '''Best-fit parameters''' || '''LD<sub>50</sub>'''<br />
|-<br />
| 1<br />
| DeDiego et al., 2008<br />
| mice/rSARS-CoV <br />
| intranasal/4<br />
| pfu<br />
| Death<br />
| exponential<br />
| k = 2.97E-03<br />
| 233.25<br />
|-<br />
| 2<br />
| De Albuquerque et al., 2006<br />
| Mice/MHV-1<br />
| intranasal/4<br />
| pfu<br />
| Death<br />
| exponential<br />
| k = 2.14E-03<br />
| 323.63<br />
|- style="background-color:#cccccc;border-top:none;border-bottom:none;border-left:none;border-right:none;padding:0.0201in;"<br />
| 1 and 2 *<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| exponential<br />
| k = 2.46E-03<br />
| 281.97<br />
|}<br />
|}<br />
<br />
'''The data from experiments 1 and 2 were able to be statistically pooled.'''<br />
<br />
----<br />
<br />
=== '''<sup>*</sup>Recommended Model''' ===<br />
<br />
It is recommended that the pooled experiments 1 and 2 should be used as the best dose-response model. Both strains are common in outbreaks. The pooling narrows the range of the confidence region of the parameter estimates and enhances the statistical precision.<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeDiego2008_SARS_datafitconf_1.xls '''Table 8.2: human/type 14 strain model data''']<br />
| Dose || Dead || Survived || Total<br />
|-<br />
| 240 || 1 || 2 || 3<br />
|-<br />
| 800 || 3 || 0 || 3<br />
|-<br />
| 2400 || 2 || 0 || 2<br />
|-<br />
| 12000 || 6 || 0 || 6<br />
|-<br />
| colspan = "4" | DeDiego et al., 2008<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeDiego2008_SARS_datafitconf_1.xls '''Table 8.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 0.97<br />
| rowspan = "2" | 9.00E-04<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''0.976''<br />
| 7.81 <br /> ''0.809''<br />
|-<br />
| Beta Poisson<br />
| 0.97<br />
| 2<br />
| 5.99 <br /> ''0.616''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeDiego2008_SARS_datafitconf_1.xls '''Table 8.4 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 2.97E-03<br />
| 0.0019<br />
| 0.0019<br />
| 0.0019<br />
| 0.0051<br />
| 0.092<br />
| 0.092<br />
|-<br />
| LD<sub>50</sub>(spores)<br />
| 233.25<br />
| 7.54<br />
| 7.54<br />
| 135.59<br />
| 364.19<br />
| 364.19<br />
| 364.19<br />
|}<br />
|}<br />
<br />
<br />
[[File:DeDiego2008_SARS_ExpHist.png|thumb|left|500px|'''Figure 8.1. Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:DeDiego2008_SARS_ExpModel.png|thumb|none|500px|'''Figure 8.2. Exponential model plot, confidence bounds around optimized model''']]<br><br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 2'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeAlbuquerque2006_SARS_datafitconf_2.xls '''Table 8.5: human/type 14 and 39 strains model data''']<br />
| Dose || Dead || Survived || Total<br />
|-<br />
| 5 || 0 || 5 || 5<br />
|-<br />
| 50 || 1 || 4 || 5<br />
|-<br />
| 500 || 3 || 2 || 5<br />
|-<br />
| 5000 || 5 || 0 || 5<br />
|-<br />
| colspan = "4" | De Albuquerque et al., 2006.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeAlbuquerque2006_SARS_datafitconf_2.xls '''Table 8.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 0.61<br />
| rowspan = "2" | 0.069<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''0.793''<br />
| 7.81 <br /> ''0.895''<br />
|-<br />
| Beta Poisson<br />
| 0.54<br />
| 2<br />
| 5.99 <br /> ''0.765''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeAlbuquerque2006_SARS_datafitconf_2.xls '''Table 8.7 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 2.14E-03<br />
| 6.25E-04<br />
| 6.55E-04<br />
| 9.06E-04<br />
| 6.58E-03<br />
| 6.58E-03<br />
| 9.86E-03<br />
|-<br />
| LD<sub>50</sub>(spores)<br />
| 323.63<br />
| 70.27<br />
| 105.32<br />
| 127.96<br />
| 765.29<br />
| 1058.68<br />
| 1109.24<br />
|}<br />
|}<br />
<br />
<br />
[[File:De Albuquerque2006_SARS_ExpHist.png|thumb|left|500px|'''Figure 8.3. Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:De Albuquerque2006_SARS_ExpModel.png|thumb|none|500px|'''Figure 8.4. Exponential model plot, confidence bounds around optimized model''']]<br><br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 1 and 2'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeDiego2008DeAlbuquerque2006_SARS_datafitconf_3.xls '''Table 8.8: human/type 39 strain model data''']<br />
| Dose || Dead || Survived || Total<br />
|-<br />
| 240 || 1 || 2 || 3<br />
|-<br />
| 800 || 3 || 0 || 3<br />
|-<br />
| 2400 || 2 || 0 || 2<br />
|-<br />
| 12000 || 6 || 0 || 6<br />
|-<br />
| 5 || 0 || 5 || 5<br />
|-<br />
| 50 || 1 || 4 || 5<br />
|-<br />
| 500 || 3 || 2 || 5<br />
|-<br />
| 5000 || 5 || 0 || 5<br />
|-<br />
| colspan = "4" | DeDiego et al., 2008 & De Albuquerque et al., 2006<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeDiego2008DeAlbuquerque2006_SARS_datafitconf_3.xls '''Table 8.9: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 1.75<br />
| rowspan = "2" | 0.0018<br />
| 7<br />
| rowspan = "2" | 3.84 <br /> ''0.966''<br />
| 14.07 <br /> ''0.972''<br />
|-<br />
| Beta Poisson<br />
| 1.75<br />
| 6<br />
| 12.59 <br /> ''0.941''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:DeDiego2008DeAlbuquerque2006_SARS_datafitconf_3.xls '''Table 8.10 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 2.46E-03<br />
| 0.0011<br />
| 0.0013<br />
| 0.0014<br />
| 0.0046<br />
| 0.0053<br />
| 0.0072<br />
|-<br />
| LD<sub>50</sub>(spores)<br />
| 281.97<br />
| 96.60<br />
| 131.50<br />
| 151.16<br />
| 513.27<br />
| 542.77<br />
| 647.46<br />
|}<br />
|}<br />
<br />
<br />
[[File:Pooling_SARS_ExpHist.png|thumb|left|500px|'''Figure 8.5. Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Pooling_SARS_ExpModel.png|thumb|none|500px|'''Figure 8.6. Exponential model plot, confidence bounds around optimized model''']]<br><br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
By increasing the number of data points, the pooling narrows the range of the confidence region of the parameter estimates and enhances the statistical precision.<br />
<br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
De Albuquerque, N., Baig, E., Ma, X., Zhang, J., He, W., Rowe, A., Habal, M., Liu, M., Shalev, I., Downey, G.P., Gorczynski, R., Butany, J., Leibowitz, J., Weiss, S.R., McGilvray, I.D., Phillips, M.J., Fish, E.N. and Levy, G.A. (2006) [http://jvi.asm.org/cgi/content/abstract/80/21/10382 Murine hepatitis virus strain 1 produces a clinically relevant model of severe acute respiratory syndrome in a/j mice] ''Journal of Virology'' '''80''', 10382-10394.<br />
<br />
DeDiego, M.L., Pewe, L., Alvarez, E., Rejas, M.T., Perlman, S. and Enjuanes, L. (2008) [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WXR-4SDNK59-1&_user=1111158&_coverDate=07%2F05%2F2008&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1634646931&_rerunOrigin=scholar.google&_acct=C000051676&_version=1&_urlVersion=0&_userid=1111158&md5=32814dabedfca7684863096ae619b9b2&searchtype=a Pathogenicity of severe acute respiratory coronavirus deletion mutants in hace-2 transgenic mice]. ''Virology'' '''376''', 379–389.<br />
<br />
Watanabe, T., Bartrand, T.A., Weir, M.H., Omura, T. and Haas, C.N. (2010) [http://onlinelibrary.wiley.com/doi/10.1111/j.1539-6924.2010.01427.x/abstract Development of a dose-response model for sars coronavirus]. ''Risk Analysis'' '''30''', 1129-1138.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Lassa_virus:_Dose_Response_Models&diff=2350Lassa virus: Dose Response Models2011-10-05T15:24:19Z<p>Yh: /* Summary Data */</p>
<hr />
<div>==<center>'''''Lassa virus'''''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://wiki.camra.msu.edu/images/0/09/Lassa.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview of ''Lassa virus'' and hemorrhagic fever'''===<br />
<br />
''Lassa virus'' is a RNA virus belonging to the family of Arenaviridae. As the causative agent of hemorrhagic fever, ''Lassa virus ''infects more than 200,000 people per year causing more than 3,000 deaths with a mortality rate of about 15% among the hospitalized cases . The U.S. Centers for Disease Control and Prevention have classified ''Lassa virus'' as a Category A bioterrorism agent for public health preparedness. <br />
<br />
Hemorrhagic fever is highly fatal disease mostly found in West Africa. The disease has an acute phase lasting 1 to 4 weeks, characterized by fever, skin rash with hemorrhages, sore throat, headache and diarrhea. It has been reported that ''Lassa virus'' infects more than 200,000 people per year with a mortality rate of about 15% among the hospital cases. <br />
<br />
Transmission of Lassa fever by direct person-to-person contact can occur via virus-contaminated blood, pharyngeal secretion, and urine of patients.<br />
<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
Jahrling et al. exposed Hartley guinea pigs (450 to 600g) to Lassa virus via subcutaneous route. Lassa virus strain Josiah was isolated in 1976 from the serum of a 40-year-old man in Sierra Leone, Africa.<br />
<br />
Stephenson et al. exposed Hartley guinea pigs (180 to 300g) to aerosolized Lassa virus strain Josiah of 4.5 μm or less in diameter generated by dynamic aerosol aerators.<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ '''Table 5.1. Summary of the lassa virus data and best fits'''<br />
| Experiment number || Reference || Host type/pathogen strain || Route/number of doses || Dose units || Response || Best-fit model || Best-fit parameters || LD<sub>50</sub><br />
|-<br />
| 1 || Stephenson et al., 1984 || guinea pig/ Josiah strain || Inhalation/4 || PFU || death || Beta-Poisson || α = 0.079, N<sub>50</sub> = 14253 || 14253<br />
|- style="background-color:#cccccc;border-top:none;border-bottom:none;border-left:none;border-right:none;padding:0.0201in;"<br />
| 2* || Jahrling et al., 1982 || guinea pig/ Josiah strain || Subcutaneous/6 || PFU || death || Exponential || k = 2.95 || 0.24<br />
|-<br />
|}<br />
|}<br />
<br />
'''The data from different experiments were not considered for pooling because of very different exposure routes.'''<br />
<br />
----<br />
<br />
=== '''<sup>*</sup>Recommended Model''' ===<br />
<br />
It is recommended that experiment 2 should be used as the best dose response model. Subcutaneous exposure is much more infective than the inhalation in this case so that it should receive more attention in terms of emergency preparedness and public intervention.<br />
<br />
==='''Optimized Models and Uncertainty and Fitting Analyses'''===<br />
<br />
'''Output for experiment 1.'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Stephenson1984_Lassa_datafitconf.xls '''Table 5.2: Guinea pig/ Josiah strain model data''']<br />
| Dose||Dead||Survived||Total<br />
|-<br />
| 5.37E+03 || 4 || 4 || 8<br />
|-<br />
| 7.24E+02 || 3 || 5 || 8<br />
|-<br />
| 4.80E+01 || 1 || 7 || 8<br />
|-<br />
| 5.00E+00 || 1 || 7 || 8<br />
|-<br />
| colspan = "4" | Stephenson et al., 1984.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Stephenson1984_Lassa_datafitconf.xls '''Table 5.3: Goodness of Fit and Model Selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 14.44<br />
| rowspan = "2" | 13.80<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''2.00E-04''<br />
| 7.81 <br /> ''0.0024''<br />
|-<br />
| Beta Poisson<br />
| 0.63<br />
| 2<br />
| 5.99 <br /> ''0.729''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Stephenson1984_Lassa_datafitconf.xls '''Table 5.4: Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.079<br />
| --||--||--||--||--||--<br />
|-<br />
| N<sub>50</sub><br />
| 14,253<br />
| --||--||--||--||--||--<br />
|-<br />
| LD<sub>50</sub>(spores)<br />
| 14,253<br />
| 0.21<br />
| 0.92<br />
| 2.90<br />
| 1.03E+15<br />
| 2.84E+19<br />
| inf<br />
|}<br />
|}<br />
<br />
<br />
[[File:Stephenson1984_lassa_bPHist.png|thumb|left|500px|'''Figure 5.1 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Stephenson1984_lassa_bPmodel.png|thumb|none|500px|'''Figure 5.2 beta Poisson model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
<br />
'''Output for experiment 2.'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Jahrling1982_Lassa_datafitconf.xls '''Table 5.5: Guinea pig/ Josiah strain model data''']<br />
| Dose||Dead||Survived||Total<br />
|-<br />
| 2.40E+05 || 5 || 0 || 5<br />
|-<br />
| 2.40E+03 || 15 || 0 || 15<br />
|-<br />
| 2.40E+01 || 10 || 0 || 10<br />
|-<br />
| 2.00E+00 || 10 || 0 || 10<br />
|-<br />
| 2.00E-01 || 4 || 6 || 10<br />
|-<br />
| 2.00E-02 || 1 || 9 || 10<br />
|-<br />
| colspan = "4" | Jahrling et al., 1982.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Jahrling1982_Lassa_datafitconf.xls '''Table 5.6: Goodness of Fit and Model Selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 0.42<br />
| rowspan = "2" | 6.00E-04<br />
| 5<br />
| rowspan = "2" | 3.84 <br /> ''0.98''<br />
| 11.07 <br /> ''0.99''<br />
|-<br />
| Beta Poisson<br />
| 0.42<br />
| 4<br />
| 9.49 <br /> ''0.98''<br />
|-<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Jahrling1982_Lassa_datafitconf.xls '''Table 5.7: Optimized parameters for the best fitting (Exponential), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 2.95<br />
| 1.37<br />
| 1.61<br />
| 1.65<br />
| 5.43<br />
| 6.48<br />
| 8.62<br />
|-<br />
| LD<sub>50</sub>(spores)<br />
| 0.24<br />
| 0.080<br />
| 0.11<br />
| 0.13<br />
| 0.42<br />
| 0.43<br />
| 0.50<br />
|}<br />
|}<br />
<br />
<br />
[[File:Jahrling1982_lassa_ExpHist.png|thumb|left|500px|'''Figure 5.3 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Jahrling1982_lassa_ExpModel.png|thumb|none|500px|'''Figure 5.4 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
Noting a significant difference of LD<sub>50</sub> between the inhalation (1.4x10<sup>4 </sup>pfu) and subcutaneous (0.2 pfu) routes has been identified, which suggests a substantial variation of virulence with infection site. This could also attribute to the difference between out-bred and in-bred origins. The very low LD<sub>50 </sub>for the subcutaneous route should be due to the uncertainties of dose counting in the original study.<br />
<br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
Djavani, M., C. Yin, L. Xia, I. Lukashevich, C. Pauza and M. Salvato (2000). [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TD4-3Y6Y3MY-J&_user=1111158&_coverDate=02%2F14%2F2000&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1618086195&_rerunOrigin=scholar.google&_acct=C000051676&_version=1&_urlVersion=0&_userid=1111158&md5=cca276e90842e2a3e5b2c2cf95a9a36a&searchtype=a "Murine immune responses to mucosally delivered Salmonella expressing Lassa fever virus nucleoprotein."] Vaccine '''18'''(15): 1543-1554.<br />
<br />
Jahrling, P. B., S. Smith, H. R.A. and J. B. Rhoderick (1982). [http://iai.asm.org/cgi/content/abstract/37/2/771 "Pathogenesis of Lassa virus infection in guinea pigs."] Infection and Immunity '''37'''(2): 771-778.<br />
<br />
Stephenson, E., A. Larson and J. Dominik (1984). [http://onlinelibrary.wiley.com/doi/10.1002/jmv.1890140402/abstract "Effect of environmental factors on induced lassa virus infection."] Journal of Medical Virology '''14''': 295-303.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=PrP_prions:_Dose_Response_Models&diff=2349PrP prions: Dose Response Models2011-10-05T15:17:18Z<p>Yh: /* Summary Data */</p>
<hr />
<div>==<center>'''Prion'''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://wiki.camra.msu.edu/images/6/68/Prion.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview of prion and prion diseases'''===<br />
<br />
Scrapie is a prion agent. Prions are 'self-replicating' basic proteins of small molecular weight. Prions form a new class of infectious agents responsible for a number of slow degenerative central nervous system diseases of humans and other animal species. The transmissible spongiform encephalopathies (TSEs) are a group of progressive neurological prion diseases, including scrapie in sheep and goats, bovine spongiform encephalopathy (BSE) in cattle and Creutzfeldt-Jakob disease (CJD) in humans (Gale 2006).<br />
<br />
Public awareness of prion diseases have been raised after an outbreak of BSE occurred among cattle in many European countries and scientific evidence indicated the foodborne transmission of BSE to humans (Will et al. 1996; Smith and Bradley 2003).<br />
<br />
The disease is most easily transmitted to humans via consuming food contaminated with the brain or spinal cord of infected carcasses.<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
Diringer et al. (1998) inoculated outbred Syrian hamsters orally with graded doses of scrapie agent. The infectious agent was prepared from the brains of scrapied hamsters at the terminal stage of disease.<br />
<br />
Jacquemot et al. (2005) exposed C57BL/6 mice to mouse-adapted scrapie strain C506M3 via the intraperitoneal route. The inoculum was a brain homogenate at 10% (wt/vol) in 5% glucose solution from a mouse with scrapie at the terminal stage of disease<br />
<br />
Taylor et al. (1995) injected Weanling RIII/FaDk-ro mice with pooled BSE-infected brain. They measured the titer of infectivity by bioassay in mice. The infectious agent was prepared from the brains of 861cattle with suspected BSE obtained between August and November 1990 from five veterinary centers throughout England.<br />
<br />
<br />
'''Table 1.1. Summary of the prion data and best fits'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
| Experiment number || Reference || Host type/pathogen strain || Route/number of doses || Dose units || Response || Best-fit model || Best-fit parameters || LD<sub>50</sub>/ID<sub>50<sub><br />
|- style="background-color:#cccccc;border-top:none;border-bottom:none;border-left:none;border-right:none;padding:0.0201in;"<br />
| 1* || Diringer et al. 1998 || hamsters/scrapie strain 263K || oral /5 || LD50 i.c. || Death || beta-Poisson || α = 1.76 N<sub>50</sub> = 1.04E+05 || 1.04E+05<br />
|-<br />
| 2 || Jacquemot et al. 2005 || mice/scrapie strain C506M3 || intraperitoneal /3 || LD50 i.c. || Death || exponential || k = 2.40E-05 || 2.89E+04<br />
|-<br />
| 3 || Taylor et al. 1995 || mice/BSE agent || Unknown type of injection/4 || ID50 unit || Infection || exponential || k = 0.69 || 1.00<br />
|}<br />
|}<br />
<br />
'''The data were not able to be statistically pooled.'''<br />
<br />
----<br />
<br />
=== '''<sup>*</sup>Recommended Model''' ===<br />
<br />
It is recommended that experiment 1 should be used as the best dose response model. The exposure was oral route which is a better representation of an actual release scenario.<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Diringer1998_Prion_datafitconf_1.xls '''Table 1.2: hamsters/scrapie strain 263K model data''']<br />
| Dose || Dead || Survival || Total<br />
|-<br />
| 200 || 0 || 40 || 40<br />
|-<br />
| 2000 || 1 || 79 || 80<br />
|-<br />
| 20000 || 9 || 71 || 80<br />
|-<br />
| 200000 || 58 || 22 || 80<br />
|-<br />
| 2000000 || 29 || 1 || 30<br />
|-<br />
| colspan = "4" | Diringer et al. 1998<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Diringer1998_Prion_datafitconf_1.xls '''Table 1.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 14.54<br />
| rowspan = "2" | 12.62<br />
| 4<br />
| rowspan = "2" | 3.84 <br /> ''4.00E-04''<br />
| 9.49 <br /> ''0.0058''<br />
|-<br />
| Beta Poisson<br />
| 1.92<br />
| 3<br />
| 7.81 <br /> ''0.589''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Diringer1998_Prion_datafitconf_1.xls '''Table 1.4 Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 1.76<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 1.04E+05<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 1.04E+05<br />
| 7.06E+04<br />
| 7.83E+04<br />
| 8.21E+04<br />
| 1.33E+05<br />
| 1.41E+05<br />
| 1.54E+05<br />
|}<br />
|}<br />
<br />
<br />
[[File:Diringer1998_prion_BPScatter.png|thumb|left|500px|'''Figure 1.1 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Diringer1998_prion_BPModel.png|thumb|none|500px|'''Figure 1.2 beta Poisson model plot, with confidence bounds around optimized model''']]<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 2'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Jacquemot2005_Prion_datafitconf_2.xls '''Table 1.5: mice/ scrapie strain C506M3 model data''']<br />
| Dose || Dead || Survival || Total<br />
|-<br />
| 125 || 0 || 11 || 11<br />
|-<br />
| 1250 || 1 || 9 || 10<br />
|-<br />
| 12500 || 2 || 8 || 10<br />
|-<br />
| colspan = "4" | Jacquemot et al. 2005<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Jacquemot2005_Prion_datafitconf_2.xls '''Table 1.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 1.34<br />
| rowspan = "2" | 0.99<br />
| 2<br />
| rowspan = "2" | 3.84 <br /> ''0.320''<br />
| 5.99 <br /> ''0.512''<br />
|-<br />
| Beta Poisson<br />
| 0.35<br />
| 1<br />
| 3.84 <br /> ''0.554''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Jacquemot2005_Prion_datafitconf_2.xls '''Table 1.7 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 2.40E-05<br />
| 1.00E-13<br />
| 1.00E-13<br />
| 7.23E-06<br />
| 5.47E-05<br />
| 5.81E-05<br />
| 7.44E-05<br />
|-<br />
| LD<sub>50</sub> (spores)<br />
| 2.89E+04<br />
| 9.32E+03<br />
| 1.19E+04<br />
| 1.27E+04<br />
| 9.58E+04<br />
| 6.92E+12<br />
| 6.92E+12<br />
|}<br />
|}<br />
<br />
<br />
[[File:Jacquemot2005_prion_ExpHist.png|thumb|left|500px|'''Figure 1.3 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Jacquemot2005_prion_ExpModel.png|thumb|none|500px|'''Figure 1.4 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 3'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Taylor1995_Prion_datafitconf_3.xls '''Table 1.8: mice/BSE agent model data''']<br />
| Dose || Infected || Non-infected || Total<br />
|-<br />
| 0.0186 || 0 || 13 || 13<br />
|-<br />
| 0.186 || 4 || 12 || 16<br />
|-<br />
| 1.856 || 9 || 5 || 14<br />
|-<br />
| 18.56 || 13 || 0 || 13<br />
|-<br />
| colspan = "4" | Taylor et al. 1995<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Taylor1995_Prion_datafitconf_3.xls '''Table 1.9: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 2.77<br />
| rowspan = "2" | 0.76<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''0.384''<br />
| 7.81 <br /> ''0.429''<br />
|-<br />
| Beta Poisson<br />
| 2.01<br />
| 2<br />
| 5.99 <br /> ''0.366''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Taylor1995_Prion_datafitconf_3.xls '''Table 1.10 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 0.69<br />
| 0.30<br />
| 0.36<br />
| 0.40<br />
| 1.16<br />
| 1.32<br />
| 1.64<br />
|-<br />
| ID<sub>50</sub><br />
| 1.00<br />
| 0.42<br />
| 0.53<br />
| 0.60<br />
| 1.73<br />
| 1.95<br />
| 2.28<br />
|}<br />
|}<br />
<br />
<br />
[[File:Taylor1995_prion_ExpHist.png|thumb|left|500px|'''Figure 1.5 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Taylor1995_prion_ExpModel.png|thumb|none|500px|'''Figure 1.6 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
One should note that the dose unit in the literature was not given as organism number or cfu/pfu, so the relative units were presented. <br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
Diringer, H., roehmel, J. and Beekes, M. (1998) [http://vir.sgmjournals.org/cgi/reprint/79/3/609 Effect of repeated oral infection of hamsters with scrapie.] ''Journal of General Virology'' '''79''', 609-612.<br />
<br />
Gale, P. (2006) [http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2672.2006.03110.x/pdf The infectivity of transmissible spongiform encephalopathy agent at low doses: The importance of phospholipid.] ''Journal of Applied Microbiology'' '''101''', 261-274.<br />
<br />
Jacquemot, C., Cuche, C., Dormont, D. and Lazarini, F. (2005) [http://jvi.asm.org/cgi/reprint/79/14/8904 High incidence of scrapie induced by repeated injections of subinfectious prion doses.] ''Journal of Virology'', 8904–8908.<br />
<br />
Smith, P. and Bradley, R. (2003) [http://bmb.oxfordjournals.org/content/66/1/185.full.pdf+html Bovine spongiform encephalopathy (bse) and its epidemiology.] ''British Medical Bulletin'' '''66''', 185–198.<br />
<br />
Taylor, D.M., Woodgate, S.L. and Atkinson, M.J. (1995) [http://veterinaryrecord.bmj.com/content/137/24/605.abstract Inactivation of the bovine spongiform encephalopathy agent by rendering procedures.] ''Veterinary Record'' '''137''', 605-610.<br />
<br />
Will, R., Ironside, J., Zeidler, M., Cousens, S., Estibeiro, K., Alperovitch, A., Poser, S., Pocchiari, M., Hofman, A. and Smith, P. (1996) [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T1B-4B8JK45-13H&_user=1111158&_coverDate=04%2F06%2F1996&_rdoc=1&_fmt=high&_orig=gateway&_origin=gateway&_sort=d&_docanchor=&view=c&_searchStrId=1702434211&_rerunOrigin=scholar.google&_acct=C000051676&_version=1&_urlVersion=0&_userid=1111158&md5=b610c4717dec09b1a602a28ecf63780f&searchtype=a A new variant of creutzfeldt-jakob disease in the uk.] ''Lancet'' '''347''', 921-925.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Influenza:_Dose_Response_Models&diff=2348Influenza: Dose Response Models2011-10-05T15:14:32Z<p>Yh: /* *Recommended Model */</p>
<hr />
<div>==<center>'''Influenza'''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://wiki.camra.msu.edu/images/c/c4/Influenza.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview '''===<br />
<br />
Influenza A viruses are members of the family ''Orthomyxoviridae'', which comprises enveloped viruses with segmented, negative-sense RNA genomes. Based on the antigenicity of the two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA), influenza A viruses are currently divided into 16 HA and 9 NA subtypes, designated as H1-H16 and N1-N9. Over the past century, viruses of the H1N1, H2N2, H3N2, and H1N2 subtypes have circulated in humans. Additionally, new subtypes such as H5N1 and H7N9 have been recently isolated from human as well as poultry. Influenza A virus is one of the most common causes of human respiratory infections and the most significant because they cause high morbidity and mortality. Transmission of influenza can be achieved via environmental reservoirs or human-to-human communication (Kitajima et al. under review; Watanabe et al. under review). <br />
<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
Murphy ''et al.'' (1984) intranasally challenged adult volunteers with influenza A (H1N1) California/10/78 cold-adpted viruses. Infection was defined as virus recovery and/or antibody response.<br />
<br />
Murphy ''et al.''(1985) challenged adult volunteers with influenza A (H3N2) Washington/897/80 avian-human reassortant viruses via intranasal route. Infection was defined as virus isolation and/or antibody response.<br />
<br />
Fan et al. (2009) exposed six-week-old SPF BALB/c mice (five mice/dose) intranasally with a highly pathogenic avian influenza A (H5N1) virus (DKGX/35 strain).<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ '''Table 4.1. Summary of the Influenza data and best fits'''<br />
| Experiment number || Reference || Host type/pathogen strain || Route/number of doses || Dose units || Response || Best-fit model || Best-fit parameters || LD<sub>50</sub><br />
|-<br />
| 1<br />
| Murphy et al., 1984<br />
| humans/H1N1,A/California/10/78 attenuated strain<br />
| intranasal/4<br />
| TCID50<br />
| infection<br />
| beta-Poisson<br />
| α = 0.90<br />
N<sub>50</sub> = 1.25E+06<br />
| 1.25E+06<br />
|-<br />
| 2<br />
| Murphy et al., 1985<br />
| humans/H3N2,A/Washington/897/80 attenuated strain<br />
| intranasal/5<br />
| TCID50<br />
| infection<br />
| beta-Poisson<br />
| α = 0.43<br />
N<sub>50</sub> = 6.66E+05<br />
| 6.66E+05<br />
|-<br />
| 3<br />
| Fan et al., 2009<br />
| mice/ H5N1, DKGX/35 strain<br />
| intranasal/6<br />
| EID50<br />
| death<br />
| exponential<br />
| k = 0.011 <br />
| 63.80<br />
|- style="background-color:#cccccc;border-top:none;border-bottom:none;border-left:none;border-right:none;padding:0.0201in;"<br />
| 1 and 2 *<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| beta-Poisson<br />
| α = 0.58<br />
N<sub>50</sub> = 9.45E+05<br />
| 9.45E+05<br />
|}<br />
|}<br />
<br />
'''The data from experiments 1 and 2 were able to be statistically pooled.'''<br />
<br />
<br />
----<br />
<br />
=== '''<sup>*</sup>Recommended Model''' ===<br />
<br />
It is recommended that the pooled experiments 1 and 2 should be used as the best dose-response model. Both strains are common in human outbreaks. The pooling narrows the range of the confidence region of the parameter estimates and enhances the statistical precision.<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984_Influenza_datafitconf_1.xls '''Table 4.2: humans/H1N1 A/California/10/78 attenuated strain model data''']<br />
| Dose|| Infected || Non-infected || Total<br />
|-<br />
| 63095.734 || 0 || 15 || 15<br />
|-<br />
| 630957.34 || 4 || 7 || 11<br />
|-<br />
| 6309573.4 || 19 || 3 || 22<br />
|-<br />
| 63095734 || 24 || 1 || 25<br />
|-<br />
| colspan = "4" | Murphy et al., 1984<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984_Influenza_datafitconf_1.xls '''Table 4.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 23.56<br />
| rowspan = "2" | 21.54<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 7.81 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 2.02<br />
| 2<br />
| 5.99 <br /> ''0.365''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984_Influenza_datafitconf_1.xls '''Table 4.4 Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.90<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 1.25E+06<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 1.25E+06<br />
| 5.03E+05<br />
| 6.27E+05<br />
| 6.94E+05<br />
| 2.39E+06<br />
| 2.72E+06<br />
| 3.36E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Murphy1984_Influenza_BPScatter.png|thumb|left|500px|'''Figure 4.1 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Murphy1984_Influenza_BPModel.png|thumb|none|500px|'''Figure 4.2 beta Poisson model plot, with confidence bounds around optimized model''']]<br><br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 2'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1985_Influenza_datafitconf_2.xls '''Table 4.5: humans/H3N2, A/Washington/897/80 attenuated strain model data''']<br />
| Dose || Infected || Non-infected || Total<br />
|-<br />
| 100000 || 2 || 10 || 12<br />
|-<br />
| 1000000 || 8 || 5 || 13<br />
|-<br />
| 10000000 || 16 || 3 || 19<br />
|-<br />
| 31622777 || 16 || 4 || 20<br />
|-<br />
| 100000000 || 19 || 0 || 19<br />
|-<br />
| colspan = "4" | Murphy et al., 1985<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1985_Influenza_datafitconf_2.xls '''Table 4.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 39.05<br />
| rowspan = "2" | 34.80<br />
| 4<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 9.49 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 4.26<br />
| 3<br />
| 7.81 <br /> ''0.235''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1985_Influenza_datafitconf_2.xls '''Table 4.7 Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.43<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 6.66E+05<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 6.66E+05<br />
| 1.41E+05<br />
| 2.17E+05<br />
| 2.63E+05<br />
| 1.55E+06<br />
| 1.80E+06<br />
| 2.33E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Murphy1985_Influenza_BPScatter.png|thumb|left|500px|'''Figure 4.3 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Murphy1985_Influenza_BPModel.png|thumb|none|500px|'''Figure 4.4 beta Poisson model plot, with confidence bounds around optimized model''']]<br><br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 3'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fan2009_Influenza_datafitconf_3.xls '''Table 4.8: mice/ H5N1,DKGX/35 strain model data''']<br />
| Dose || Infected || Non-infected || Total<br />
|-<br />
| 10 || 1 || 4 || 5<br />
|-<br />
| 100 || 3 || 2 || 5<br />
|-<br />
| 1000 || 5 || 0 || 5<br />
|-<br />
| 10000 || 5 || 0 || 5<br />
|-<br />
| 100000 || 5 || 0 || 5<br />
|-<br />
| 1000000 || 5 || 0 || 5<br />
|-<br />
| colspan = "4" | Fan et al., 2009.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fan2009_Influenza_datafitconf_3.xls '''Table 4.9: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 0.50<br />
| rowspan = "2" | 0.092<br />
| 5<br />
| rowspan = "2" | 3.84 <br /> ''0.762''<br />
| 11.07 <br /> ''0.992''<br />
|-<br />
| Beta Poisson<br />
| 0.41<br />
| 4<br />
| 9.49 <br /> ''0.982''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fan2009_Influenza_datafitconf_3.xls '''Table 4.10 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 0.011<br />
| 0.0031<br />
| 0.0033<br />
| 0.0046<br />
| 0.035<br />
| 0.035<br />
| 0.054<br />
|-<br />
| LD<sub>50</sub> (spores)<br />
| 63.80<br />
| 12.78<br />
| 19.94<br />
| 19.94<br />
| 151.94<br />
| 210.65<br />
| 220.81<br />
|}<br />
|}<br />
<br />
<br />
[[File:Fan2009_Influenza_ExpHist.png|thumb|left|500px|'''Figure 4.5 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Fan2009_Influenza_ExpModel.png|thumb|none|500px|'''Figure 4.6 Exponential model plot, with confidence bounds around optimized model''']] <br><br />
<br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 4 '''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984Murphy1985_Influenza_datafitconf_4.xls '''Table 4.11: humans/ H1N1 A/California/10/78 and H3N2, A/Washington/897/80 attenuated strain model data''']<br />
| Dose|| Infected || Non-infected || Total<br />
|-<br />
| 100000 || 2 || 10 || 12<br />
|-<br />
| 1000000 || 8 || 5 || 13<br />
|-<br />
| 10000000 || 16 || 3 || 19<br />
|-<br />
| 31622777 || 16 || 4 || 20<br />
|-<br />
| 100000000 || 19 || 0 || 19<br />
|-<br />
| 63095.734 || 0 || 15 || 15<br />
|-<br />
| 630957.34 || 4 || 7 || 11<br />
|-<br />
| 6309573.4 || 19 || 3 || 22<br />
|-<br />
| 63095734 || 24 || 1 || 25<br />
|-<br />
| colspan = "4" | Murphy et al., 1984 & Murphy et al., 1985<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984Murphy1985_Influenza_datafitconf_4.xls '''Table 4.12: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 63.99<br />
| rowspan = "2" | 55.43<br />
| 8<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 15.51 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 8.56<br />
| 7<br />
| 14.07 <br /> ''0.286''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984Murphy1985_Influenza_datafitconf_4.xls '''Table 4.13 Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.58<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 9.45E+05<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 9.45E+05<br />
| 4.25E+05<br />
| 5.13E+05<br />
| 5.72E+05<br />
| 1.59E+06<br />
| 1.75E+06<br />
| 2.09E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Pooling_influenza_BPScatter.png|thumb|left|500px|'''Figure 4.7 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Pooling_influenza_BPModel.png|thumb|none|500px|'''Figure 4.8 beta Poisson model plot, with confidence bounds around optimized model''']]<br><br />
<br />
<br />
----<br />
<br />
==='''Advanced Dose Response Model'''===<br />
<br />
Incorporating the time postinoculation into the classical dose-response models for microbial infection generates a class of time-dose-response (TDR) models. The parameter k in the exponential dose-response model (equation 1) and the parameter N50 in the beta-Poisson model (equation 2) were set equal to functions of time that represent in vivo bacterial kinetics. Equations 1-2 with candidate G(t; θ,…) were fit to the time-dependent dose response data from experiment 3. The beta-Poisson TDR model (equation 2) incorporating an exponential-inverse-power distribution provided the best fit to the data. In Fig. 4.9, the best TDR models are plotted to compare with the observed mortalities (Kitajima et al. under review). As shown, the clear difference between the different times postinoculation gives a visible representation of the quantified results that the modification added to the classical models has a substantial effect on the dose response.<br />
<br />
<br />
[[File:TDR influenza2.png|thumb|left|800px|'''FIG. 4.9. The best-fit TDR model (curves) compared to observed mortalities against doses (symbols) from experiment 3.''']]<br />
[[File:Equation influenza.png|thumb|left|500px]]<br />
<br />
<br style="clear:both" /><br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
The pooling results indicate that the human responses to HIN1 and H3N2 viruses may have similar patterns. <br />
<br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
Fan, S., Deng, G., Song, J., Tian, G., Suo, Y., Jiang, Y., Guan, Y., Bu, Z., Kawaoka, Y. and Chen, H. (2009) [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WXR-4V87DHS-2&_user=1111158&_coverDate=02%2F05%2F2009&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1634415473&_rerunOrigin=scholar.google&_acct=C000051676&_version=1&_urlVersion=0&_userid=1111158&md5=5b307e00c73b917b2ab4aef72272df8b&searchtype=a Two amino acid residues in the matrix protein m1 contribute to the virulence difference of h5n1 avian influenza viruses in mice]. ''Virology'' '''384''', 28-32.<br />
<br />
Kitajima, M., Huang, Y., Watanabe, T., Katayama, H. and Haas, C.N. (under review) Dose-response time modeling for highly pathogenic avian influenza a (h5n1) virus infection. ''Letters in Applied Microbiology''.<br />
<br />
Murphy, B.R., Clements, M.L., Madore, H.P., Steinberg, J., O'Donnell, S., Betts, R., Demico, D., Reichman, R.C., Dolin, R. and Maassab, H.F. (1984) [http://www.ncbi.nlm.nih.gov/pubmed/6726007 Dose response of cold-adapted, reassortant influenza a/california/10/78 virus (h1n1) in adult volunteers]. ''Journal of Infectious Diseases'' '''149''', 816.<br />
<br />
Murphy, B.R., Clements, M.L., Tierney, E.L., Black, R.E., Stienberg, J. and Chanock, R.M. (1985) [http://www.jstor.org/stable/30104663 Dose response of influenza a/washington/897/80 (h3n2) avian-human reassortant virus in adult volunteers]. ''Journal of Infectious Diseases'' '''152''', 225-229.<br />
<br />
Watanabe, T., Bartrand, T.A., Omura, T. and Haas, C.N. (under review) Dose-response assessment for influenza a virus based on the datasets of infection with its live attenuated reassortants. ''Risk Analysis''.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Influenza:_Dose_Response_Models&diff=2347Influenza: Dose Response Models2011-10-05T15:14:13Z<p>Yh: /* Summary Data */</p>
<hr />
<div>==<center>'''Influenza'''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://wiki.camra.msu.edu/images/c/c4/Influenza.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview '''===<br />
<br />
Influenza A viruses are members of the family ''Orthomyxoviridae'', which comprises enveloped viruses with segmented, negative-sense RNA genomes. Based on the antigenicity of the two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA), influenza A viruses are currently divided into 16 HA and 9 NA subtypes, designated as H1-H16 and N1-N9. Over the past century, viruses of the H1N1, H2N2, H3N2, and H1N2 subtypes have circulated in humans. Additionally, new subtypes such as H5N1 and H7N9 have been recently isolated from human as well as poultry. Influenza A virus is one of the most common causes of human respiratory infections and the most significant because they cause high morbidity and mortality. Transmission of influenza can be achieved via environmental reservoirs or human-to-human communication (Kitajima et al. under review; Watanabe et al. under review). <br />
<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
Murphy ''et al.'' (1984) intranasally challenged adult volunteers with influenza A (H1N1) California/10/78 cold-adpted viruses. Infection was defined as virus recovery and/or antibody response.<br />
<br />
Murphy ''et al.''(1985) challenged adult volunteers with influenza A (H3N2) Washington/897/80 avian-human reassortant viruses via intranasal route. Infection was defined as virus isolation and/or antibody response.<br />
<br />
Fan et al. (2009) exposed six-week-old SPF BALB/c mice (five mice/dose) intranasally with a highly pathogenic avian influenza A (H5N1) virus (DKGX/35 strain).<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ '''Table 4.1. Summary of the Influenza data and best fits'''<br />
| Experiment number || Reference || Host type/pathogen strain || Route/number of doses || Dose units || Response || Best-fit model || Best-fit parameters || LD<sub>50</sub><br />
|-<br />
| 1<br />
| Murphy et al., 1984<br />
| humans/H1N1,A/California/10/78 attenuated strain<br />
| intranasal/4<br />
| TCID50<br />
| infection<br />
| beta-Poisson<br />
| α = 0.90<br />
N<sub>50</sub> = 1.25E+06<br />
| 1.25E+06<br />
|-<br />
| 2<br />
| Murphy et al., 1985<br />
| humans/H3N2,A/Washington/897/80 attenuated strain<br />
| intranasal/5<br />
| TCID50<br />
| infection<br />
| beta-Poisson<br />
| α = 0.43<br />
N<sub>50</sub> = 6.66E+05<br />
| 6.66E+05<br />
|-<br />
| 3<br />
| Fan et al., 2009<br />
| mice/ H5N1, DKGX/35 strain<br />
| intranasal/6<br />
| EID50<br />
| death<br />
| exponential<br />
| k = 0.011 <br />
| 63.80<br />
|- style="background-color:#cccccc;border-top:none;border-bottom:none;border-left:none;border-right:none;padding:0.0201in;"<br />
| 1 and 2 *<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| beta-Poisson<br />
| α = 0.58<br />
N<sub>50</sub> = 9.45E+05<br />
| 9.45E+05<br />
|}<br />
|}<br />
<br />
'''The data from experiments 1 and 2 were able to be statistically pooled.'''<br />
<br />
<br />
----<br />
<br />
=== '''<sup>*</sup>Recommended Model''' ===<br />
<br />
It is recommended that the pooled experiment 1 and 2 should be used as the best dose-response model. Both strains are common in human outbreaks. The pooling narrows the range of the confidence region of the parameter estimates and enhances the statistical precision.<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984_Influenza_datafitconf_1.xls '''Table 4.2: humans/H1N1 A/California/10/78 attenuated strain model data''']<br />
| Dose|| Infected || Non-infected || Total<br />
|-<br />
| 63095.734 || 0 || 15 || 15<br />
|-<br />
| 630957.34 || 4 || 7 || 11<br />
|-<br />
| 6309573.4 || 19 || 3 || 22<br />
|-<br />
| 63095734 || 24 || 1 || 25<br />
|-<br />
| colspan = "4" | Murphy et al., 1984<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984_Influenza_datafitconf_1.xls '''Table 4.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 23.56<br />
| rowspan = "2" | 21.54<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 7.81 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 2.02<br />
| 2<br />
| 5.99 <br /> ''0.365''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984_Influenza_datafitconf_1.xls '''Table 4.4 Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.90<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 1.25E+06<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 1.25E+06<br />
| 5.03E+05<br />
| 6.27E+05<br />
| 6.94E+05<br />
| 2.39E+06<br />
| 2.72E+06<br />
| 3.36E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Murphy1984_Influenza_BPScatter.png|thumb|left|500px|'''Figure 4.1 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Murphy1984_Influenza_BPModel.png|thumb|none|500px|'''Figure 4.2 beta Poisson model plot, with confidence bounds around optimized model''']]<br><br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 2'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1985_Influenza_datafitconf_2.xls '''Table 4.5: humans/H3N2, A/Washington/897/80 attenuated strain model data''']<br />
| Dose || Infected || Non-infected || Total<br />
|-<br />
| 100000 || 2 || 10 || 12<br />
|-<br />
| 1000000 || 8 || 5 || 13<br />
|-<br />
| 10000000 || 16 || 3 || 19<br />
|-<br />
| 31622777 || 16 || 4 || 20<br />
|-<br />
| 100000000 || 19 || 0 || 19<br />
|-<br />
| colspan = "4" | Murphy et al., 1985<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1985_Influenza_datafitconf_2.xls '''Table 4.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 39.05<br />
| rowspan = "2" | 34.80<br />
| 4<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 9.49 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 4.26<br />
| 3<br />
| 7.81 <br /> ''0.235''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1985_Influenza_datafitconf_2.xls '''Table 4.7 Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.43<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 6.66E+05<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 6.66E+05<br />
| 1.41E+05<br />
| 2.17E+05<br />
| 2.63E+05<br />
| 1.55E+06<br />
| 1.80E+06<br />
| 2.33E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Murphy1985_Influenza_BPScatter.png|thumb|left|500px|'''Figure 4.3 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Murphy1985_Influenza_BPModel.png|thumb|none|500px|'''Figure 4.4 beta Poisson model plot, with confidence bounds around optimized model''']]<br><br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 3'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fan2009_Influenza_datafitconf_3.xls '''Table 4.8: mice/ H5N1,DKGX/35 strain model data''']<br />
| Dose || Infected || Non-infected || Total<br />
|-<br />
| 10 || 1 || 4 || 5<br />
|-<br />
| 100 || 3 || 2 || 5<br />
|-<br />
| 1000 || 5 || 0 || 5<br />
|-<br />
| 10000 || 5 || 0 || 5<br />
|-<br />
| 100000 || 5 || 0 || 5<br />
|-<br />
| 1000000 || 5 || 0 || 5<br />
|-<br />
| colspan = "4" | Fan et al., 2009.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fan2009_Influenza_datafitconf_3.xls '''Table 4.9: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 0.50<br />
| rowspan = "2" | 0.092<br />
| 5<br />
| rowspan = "2" | 3.84 <br /> ''0.762''<br />
| 11.07 <br /> ''0.992''<br />
|-<br />
| Beta Poisson<br />
| 0.41<br />
| 4<br />
| 9.49 <br /> ''0.982''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fan2009_Influenza_datafitconf_3.xls '''Table 4.10 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 0.011<br />
| 0.0031<br />
| 0.0033<br />
| 0.0046<br />
| 0.035<br />
| 0.035<br />
| 0.054<br />
|-<br />
| LD<sub>50</sub> (spores)<br />
| 63.80<br />
| 12.78<br />
| 19.94<br />
| 19.94<br />
| 151.94<br />
| 210.65<br />
| 220.81<br />
|}<br />
|}<br />
<br />
<br />
[[File:Fan2009_Influenza_ExpHist.png|thumb|left|500px|'''Figure 4.5 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Fan2009_Influenza_ExpModel.png|thumb|none|500px|'''Figure 4.6 Exponential model plot, with confidence bounds around optimized model''']] <br><br />
<br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 4 '''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984Murphy1985_Influenza_datafitconf_4.xls '''Table 4.11: humans/ H1N1 A/California/10/78 and H3N2, A/Washington/897/80 attenuated strain model data''']<br />
| Dose|| Infected || Non-infected || Total<br />
|-<br />
| 100000 || 2 || 10 || 12<br />
|-<br />
| 1000000 || 8 || 5 || 13<br />
|-<br />
| 10000000 || 16 || 3 || 19<br />
|-<br />
| 31622777 || 16 || 4 || 20<br />
|-<br />
| 100000000 || 19 || 0 || 19<br />
|-<br />
| 63095.734 || 0 || 15 || 15<br />
|-<br />
| 630957.34 || 4 || 7 || 11<br />
|-<br />
| 6309573.4 || 19 || 3 || 22<br />
|-<br />
| 63095734 || 24 || 1 || 25<br />
|-<br />
| colspan = "4" | Murphy et al., 1984 & Murphy et al., 1985<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984Murphy1985_Influenza_datafitconf_4.xls '''Table 4.12: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 63.99<br />
| rowspan = "2" | 55.43<br />
| 8<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 15.51 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 8.56<br />
| 7<br />
| 14.07 <br /> ''0.286''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Murphy1984Murphy1985_Influenza_datafitconf_4.xls '''Table 4.13 Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.58<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 9.45E+05<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 9.45E+05<br />
| 4.25E+05<br />
| 5.13E+05<br />
| 5.72E+05<br />
| 1.59E+06<br />
| 1.75E+06<br />
| 2.09E+06<br />
|}<br />
|}<br />
<br />
<br />
[[File:Pooling_influenza_BPScatter.png|thumb|left|500px|'''Figure 4.7 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Pooling_influenza_BPModel.png|thumb|none|500px|'''Figure 4.8 beta Poisson model plot, with confidence bounds around optimized model''']]<br><br />
<br />
<br />
----<br />
<br />
==='''Advanced Dose Response Model'''===<br />
<br />
Incorporating the time postinoculation into the classical dose-response models for microbial infection generates a class of time-dose-response (TDR) models. The parameter k in the exponential dose-response model (equation 1) and the parameter N50 in the beta-Poisson model (equation 2) were set equal to functions of time that represent in vivo bacterial kinetics. Equations 1-2 with candidate G(t; θ,…) were fit to the time-dependent dose response data from experiment 3. The beta-Poisson TDR model (equation 2) incorporating an exponential-inverse-power distribution provided the best fit to the data. In Fig. 4.9, the best TDR models are plotted to compare with the observed mortalities (Kitajima et al. under review). As shown, the clear difference between the different times postinoculation gives a visible representation of the quantified results that the modification added to the classical models has a substantial effect on the dose response.<br />
<br />
<br />
[[File:TDR influenza2.png|thumb|left|800px|'''FIG. 4.9. The best-fit TDR model (curves) compared to observed mortalities against doses (symbols) from experiment 3.''']]<br />
[[File:Equation influenza.png|thumb|left|500px]]<br />
<br />
<br style="clear:both" /><br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
The pooling results indicate that the human responses to HIN1 and H3N2 viruses may have similar patterns. <br />
<br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
Fan, S., Deng, G., Song, J., Tian, G., Suo, Y., Jiang, Y., Guan, Y., Bu, Z., Kawaoka, Y. and Chen, H. (2009) [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WXR-4V87DHS-2&_user=1111158&_coverDate=02%2F05%2F2009&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1634415473&_rerunOrigin=scholar.google&_acct=C000051676&_version=1&_urlVersion=0&_userid=1111158&md5=5b307e00c73b917b2ab4aef72272df8b&searchtype=a Two amino acid residues in the matrix protein m1 contribute to the virulence difference of h5n1 avian influenza viruses in mice]. ''Virology'' '''384''', 28-32.<br />
<br />
Kitajima, M., Huang, Y., Watanabe, T., Katayama, H. and Haas, C.N. (under review) Dose-response time modeling for highly pathogenic avian influenza a (h5n1) virus infection. ''Letters in Applied Microbiology''.<br />
<br />
Murphy, B.R., Clements, M.L., Madore, H.P., Steinberg, J., O'Donnell, S., Betts, R., Demico, D., Reichman, R.C., Dolin, R. and Maassab, H.F. (1984) [http://www.ncbi.nlm.nih.gov/pubmed/6726007 Dose response of cold-adapted, reassortant influenza a/california/10/78 virus (h1n1) in adult volunteers]. ''Journal of Infectious Diseases'' '''149''', 816.<br />
<br />
Murphy, B.R., Clements, M.L., Tierney, E.L., Black, R.E., Stienberg, J. and Chanock, R.M. (1985) [http://www.jstor.org/stable/30104663 Dose response of influenza a/washington/897/80 (h3n2) avian-human reassortant virus in adult volunteers]. ''Journal of Infectious Diseases'' '''152''', 225-229.<br />
<br />
Watanabe, T., Bartrand, T.A., Omura, T. and Haas, C.N. (under review) Dose-response assessment for influenza a virus based on the datasets of infection with its live attenuated reassortants. ''Risk Analysis''.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Enteroviruses:_Dose_Response_Models&diff=2346Enteroviruses: Dose Response Models2011-10-05T15:09:13Z<p>Yh: /* Summary Data */</p>
<hr />
<div>==<center>'''Enteroviruses'''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://wiki.camra.msu.edu/images/b/b8/Enterovirus.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview '''===<br />
<br />
Enterovirus, a kind of small (30 nm), nonenveloped, single-stranded RNA viruses, belongs to the family ''Picornaviridae''. While most of the enterovirus infections are relatively mild and result in complete recovery of the patient, they can also cause severe and fatal diseases such as meningitis, encephalitis, myocarditis, neonatal sepsis, and polio. Infection occurs mainly via fecal-oral transmission and less commonly by respiratory droplets. While no known non-human reservoirs have been identified, water-borne, foodborne, and blood-borne transmissions have been reported (Stalkup and Chilukuri 2002).<br />
<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
Cliver (1981) challenged pigs with Porcine enterovirus type 3 and 7 via oral exposure route.<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ '''Table 3.1. Summary of the enterovirus data and best fits'''<br />
| Experiment number || Reference || Host type/pathogen strain || Route/number of doses || Dose units || Response || Best-fit model || Optimized parameters || ID<sub>50</sub><br />
|-<br />
| 1 || Cliver, 1981 || pigs/ Porcine enterovirus type 3 || oral/3 || pfu || infection || Exponential || k = 2.96E-04 || 2340.15<br />
|- style="background-color:#cccccc;border-top:none;border-bottom:none;border-left:none;border-right:none;padding:0.0201in;"<br />
| 2* || Cliver, 1981 || pigs/ Porcine enterovirus type 7 || oral/3 || pfu || infection || Exponential || k = 3.75E-03 || 185.10<br />
|}<br />
|}<br />
<br />
'''The data from different experiments were not able to be statistically pooled.'''<br />
<br />
----<br />
<br />
=== '''<sup>*</sup>Recommended Model''' ===<br />
<br />
It is recommended that experiment 1 should be used as the best dose-response model. A more virulent strain in experiment 1 can be more meaningful for emergency preparedness.<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Cliver1981_Enterovirus_datafitconf_1.xls '''Table 3.2. Pigs/ Porcine enterovirus type 3 Strain model data''']<br />
| Dose || Infected || Non-infected || Total<br />
|-<br />
| 1.00E+02 || 0 || 3 || 3<br />
|-<br />
| 2.50E+02 || 0 || 6 || 6<br />
|-<br />
| 1.00E+03 || 2 || 4 || 6<br />
|-<br />
| colspan = "4" | Cliver, 1981.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Cliver1981_Enterovirus_datafitconf_1.xls '''Table 3.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 1.24<br />
| rowspan = "2" | 3.00E-04<br />
| 2<br />
| rowspan = "2" | 3.84 <br /> ''0.986''<br />
| 5.99 <br /> ''0.537''<br />
|-<br />
| Beta Poisson<br />
| 1.24<br />
| 1<br />
| 3.84 <br /> ''0.265''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Cliver1981_Enterovirus_datafitconf_1.xls '''Table 3.4 Optimized parameters for the best fitting (Exponential), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 2.96E-04<br />
| 2.40E-17<br />
| 2.40E-17<br />
| 2.40E-17<br />
| 7.19E-04<br />
| 7.19E-04<br />
| 1.03E-03<br />
|-<br />
| LD<sub>50</sub> (spores)<br />
| 2340.15<br />
| 676.57<br />
| 963.88<br />
| 963.88<br />
| 2.89E+16<br />
| 2.89E+16<br />
| 2.89E+16<br />
|}<br />
|}<br />
<br />
<br />
[[File:Cliver1981(1)_Enterovirus_ExpHist.png|thumb|left|500px|'''Figure 3.1 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Cliver1981(1)_Enterovirus_ExpModel.png|thumb|none|500px|'''Figure 3.2 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 2'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Cliver1981_Enterovirus_datafitconf_2.xls '''Table 3.5 pigs/ Porcine enterovirus type 7''']<br />
| Dose|| Infected || Non-infected || Total<br />
|-<br />
| 2.50E+02 || 4 || 2 || 6<br />
|-<br />
| 2.50E+02 || 3 || 3 || 6<br />
|-<br />
| 1.00E+03 || 5 || 0 || 5<br />
|-<br />
| colspan = "4" | Cliver, 1981.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Cliver1981_Enterovirus_datafitconf_2.xls '''Table 3.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 0.61<br />
| rowspan = "2" | 1.00E-04<br />
| 2<br />
| rowspan = "2" | 3.84 <br /> ''0.994''<br />
| 5.99 <br /> ''0.736''<br />
|-<br />
| Beta Poisson<br />
| 0.61<br />
| 1<br />
| 3.84 <br /> ''0.433''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Cliver1981_Enterovirus_datafitconf_2.xls '''Table 3.7 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 3.75E-03<br />
| 1.83E-03<br />
| 2.19E-03<br />
| 2.19E-03<br />
| 5.62E-03<br />
| 5.62E-03<br />
| 5.62E-03<br />
|-<br />
| LD<sub>50</sub> (spores)<br />
| 185.10<br />
| 123.36<br />
| 123.36<br />
| 123.36<br />
| 316.32<br />
| 316.32<br />
| 378.96<br />
|}<br />
|}<br />
<br />
<br />
[[File:Cliver1981(2)_Enterovirus_ExpHist.png|thumb|left|500px|'''Figure 3.3 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Cliver1981(2)_Enterovirus_ExpModel.png|thumb|none|500px|'''Figure 3.4 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
The different LD<sub>50</sub> for these two experiments indicates various virulence between pathogen strains.<br />
<br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
Cliver, D. O. (1981). "Experimental infection by waterborne enteroviruses." Journal of Food Protection '''44''': 861-865.<br />
<br />
Stalkup, J. R. and S. Chilukuri (2002). "[http://www.ncbi.nlm.nih.gov/pubmed/12120436 Enterovirus infections: a review of clinical presentation, diagnosis, and treatment.]" Dermatologic clinics '''20'''(2): 217-223.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Coxiella_burnetii:_Dose_Response_Models&diff=2345Coxiella burnetii: Dose Response Models2011-10-05T15:07:17Z<p>Yh: /* Summary Data */</p>
<hr />
<div>==<center>'''''Coxiella burnetii'''''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://wiki.camra.msu.edu/images/a/a4/Coxiellaburnetii.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview of ''Coxiella burnetii'' and Q fever'''===<br />
<br />
''Coxiella burnetii'' (''C. burnetii''), an obligate intracellular gram-negative bacterium, is the causative agent of Q fever. ''C. burnetii'' multiplies only within the phagolysosomal vacuoles, particularly the macrophages of the host. During natural infections, the organism grows to high numbers in placental tissues of animals such as goats, sheep, and cows. The Center for Disease Control and Prevention (CDC) has classified ''C. burnetii'' as a category B biological terrorist agent because it consistently causes disability, can be manufactured on a large scale, remains stable under production, storage, and transportation conditions, can be efficiently disseminated and remains viable for years after dissemination.<br />
<br />
Q fever, a zoonotic disease found worldwide, may manifest as acute or chronic disease. The acute form is generally not fatal and manifests as self-controlled febrile illness. Chronic Q fever is usually characterized by endocarditis. Many animal models, including humans, have been studied for Q fever infection through various exposure routes.<br />
<br />
Humans are infected primarily through inhalation of aerosolized ''C. burnetii'' with as few as 10 organisms causing disease. Aerosols, or airborne particles, easily cause infection even without contact with infected animals, whereas person-to-person infection is rare. Ingestion of contaminated dairy products or bites from infected ticks may also lead to infection but these modes of transmission are very rare. However, there have been some recorded cases of human Q fever caused by the consumption of unpasteurized goat milk products (Tamrakar et al. 2011).<br />
<br />
----<br />
<br />
==='''Summary Data '''===<br />
<br />
Williams and Cantrell interperitoneally inoculated groups of C57BL/10ScN male mice with 11 different doses of ''C. burnetii'' phase I Ohio strain to develop a vaccine against Q fever.<br />
<br />
Scott and Williams examined the susceptibility of inbred mice to infection by ''C. burnetii ''Nine mile phase I strain. As many as 47 strains of inbred mice were evaluated. Groups of resistant C57BL/6J mice were inoculated with mean doses ranging from 10<sup>−1.3</sup> to 10<sup>7</sup> organisms. The mortalities at various doses were recorded.<br />
<br />
<br />
'''Table 4.1. Summary of the ''Coxiella burnetii'' data and best fits'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
| Experiment number || Reference || Host type/pathogen strain || Route/number of doses || Dose units || Response || Best-fit model || Best-fit parameters || LD<sub>50</sub><br />
|- style="background-color:#cccccc;border-top:none;border-bottom:none;border-left:none;border-right:none;padding:0.0201in;"<br />
| 1* || Williams et al., 1982 || mice/ phase I Ohio strain || interperitoneal/10 || No. of organisms || death || Beta-Poisson || α = 0.36, N<sub>50</sub> = 4.93E+08 || 4.93E+08<br />
|-<br />
| 2 || Scott et al., 1987 || mice/ Nine mile phase I strain || interperitoneal/13 || No. of organisms || death || Exponential || K=5.70E-11 || 1.22E+10<br />
|}<br />
|}<br />
<br />
'''The data from different experiments were not able to be statistically pooled.'''<br />
<br />
----<br />
<br />
=== '''<sup>*</sup>Recommended Model''' ===<br />
<br />
It is recommended that experiment 1 should be used as the best dose-response model. A more virulent strain in experiment 1 can be more meaningful for emergency preparedness. Also, single host strain was used in experiment 1 instead of multiple strains as in experiment 2.<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Williams1982_CoxiellaBurnetii_datafitconf_1.xls '''Table 4.2. Mice/ phase I Ohio strain model data''']<br />
| Dose|| Dead || Survived || Total<br />
|-<br />
| 7.00E+10 || 19 || 1 || 20<br />
|-<br />
| 7.00E+09 || 23 || 7 || 30<br />
|-<br />
| 7.00E+08 || 16 || 14 || 30<br />
|-<br />
| 7.00E+07 || 6 || 24 || 30<br />
|-<br />
| 7.00E+06 || 1 || 19 || 20<br />
|-<br />
| 7.00E+05 || 0 || 30 || 30<br />
|-<br />
| 7.00E+03 || 0 || 30 || 30<br />
|-<br />
| 7.00E+01 || 0 || 30 || 30<br />
|-<br />
| 7.00E+00 || 0 || 20 || 20<br />
|-<br />
| 7.00E-01 || 0 || 30 || 30<br />
|-<br />
| colspan = "4" | Williams et al., 1982.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Williams1982_CoxiellaBurnetii_datafitconf_1.xls '''Table 4.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 73.87<br />
| rowspan = "2" | 72.76<br />
| 9<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 16.92 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 1.11<br />
| 8<br />
| 15.51 <br /> ''0.998''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Williams1982_CoxiellaBurnetii_datafitconf_1.xls '''Table 4.4 Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.36<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 4.93E+08<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 4.93E+08<br />
| 1.91E+08<br />
| 2.41E+08<br />
| 2.73E+08<br />
| 9.37E+08<br />
| 1.08E+09<br />
| 1.39E+09<br />
|}<br />
|}<br />
<br />
<br />
[[File:Williams1982_Coxiella burnetii_bpHist.png|thumb|left|500px|'''Figure 4.1 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.''']][[File:Williams1982_Coxiella burnetii_bpModel.png|thumb|none|500px|'''Figure 4.2 beta Poisson model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 2'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Scott1987_CoxiellaBurnetii_datafitconf_2.xls '''Table 8.5: Dose response data''']<br />
| Dose || Dead || Survived|| Total<br />
|-<br />
| 5.01E+10 || 9 || 1 || 10<br />
|-<br />
| 5.01E+09 || 3 || 7 || 10<br />
|-<br />
| 5.01E+08 || 1 || 9 || 10<br />
|-<br />
| 5.01E+07 || 0 || 10 || 10<br />
|-<br />
| 5.01E+06 || 0 || 10 || 10<br />
|-<br />
| 5.01E+05 || 0 || 10 || 10<br />
|-<br />
| 5.01E+04 || 0 || 10 || 10<br />
|-<br />
| 5.01E+03 || 0 || 10 || 10<br />
|-<br />
| 5.01E+02 || 0 || 10 || 10<br />
|-<br />
| 5.00E+01 || 0 || 10 || 10<br />
|-<br />
| 5.00E+00 || 0 || 10 || 10<br />
|-<br />
| 5.00E-01 || 0 || 10 || 10<br />
|-<br />
| 5.00E-02 || 0 || 10 || 10<br />
|-<br />
| colspan = "4" | Scott et al., 1987.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Scott1987_CoxiellaBurnetii_datafitconf_2.xls '''Table 4.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 1.63<br />
| rowspan = "2" | 0.94<br />
| 12<br />
| rowspan = "2" | 3.84 <br /> ''0.333''<br />
| 21.03 <br /> ''1''<br />
|-<br />
| Beta Poisson<br />
| 0.69<br />
| 11<br />
| 19.68 <br /> ''1''<br />
|-<br />
| colspan = "6" | '''Exponential is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Scott1987_CoxiellaBurnetii_datafitconf_2.xls '''Table 4.7 Optimized parameters for the best fitting (exponential), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| k<br />
| 5.70E-11<br />
| 2.30E-11<br />
| 2.91E-11<br />
| 3.31E-11<br />
| 1.38E-10<br />
| 1.56E-10<br />
| 2.13E-10<br />
|-<br />
| LD<sub>50</sub><br />
| 1.22E+10<br />
| 3.25E+9<br />
| 4.45E+9<br />
| 5.02E+9<br />
| 2.09E+10<br />
| 2.36E+10<br />
| 3.02E+10<br />
|}<br />
|}<br />
<br />
<br />
[[File:Scott1987_Coxiella burnetii_ExpHist.png|thumb|left|500px|'''Figure 4.3 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Scott1987_Coxiella burnetii_ExpModel.png|thumb|none|500px|'''Figure 4.4 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
Noting an apparent difference of LD<sub>50</sub> between the experiment 1 (4.93x10<sup>8</sup>) and experiment 2 (1.22 x10<sup>10</sup>) routes has been identified. This may reflect the difference of susceptibilities associated with different host and pathogen strains.<br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
Scott, G. and J. C. Williams (1987). "[http://mic.sgmjournals.org/cgi/content/abstract/133/3/691 Pathological responses of inbred mice to phase I Coxiella Burnetii.]" Journal of General Microbiology '''133'''(3): 691–700.<br />
<br />
Tamrakar, S. B., A. Haluska, C. N. Haas and T. A. Bartrand (2011). "[http://onlinelibrary.wiley.com/doi/10.1111/j.1539-6924.2010.01466.x/full Dose-Response Model of Coxiella burnetii (Q Fever)]." Risk Analysis '''31'''(1): 120-128.<br />
<br />
Williams, J. C. and J. L. Cantrell (1982). "[http://iai.asm.org/cgi/content/abstract/35/3/1091 Biological and immunological properties of Coxiella burnetii vaccines in C57BL/1OScN endotoxin-nonresponder mice]." Infection and Immunity '''35'''(3): 1091–1102.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2313Dose response assessment2011-10-04T06:26:01Z<p>Yh: /* Types of Models */</p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 can be given as P(response) = 1- exp(-dose/r) <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Dose_response_assessment&diff=2312Dose response assessment2011-10-04T06:24:34Z<p>Yh: /* Types of Models */</p>
<hr />
<div>==Dose Response==<br />
In the QMRA framework, the dose response assessment phase is the quantitative yardstick for the risk estimate, as this phase estimates a risk of response (infection, illness or death) with respect to a known dose of a pathogen. The basis of the dose response phase is the dose response models, which are mathematical functions derived to describe the dose response relationship for specific pathogens. Therefore, for a particular endpoint (response), a specific pathogen and exposure route there is a unique dose response relationship and consequently a dose response model. Dose response models are necessary as it is not possible to perform a direct study (even with animals) to assess dose corresponding to an acceptably low risk. <br />
<br />
===Dose Response Models===<br />
<br />
To be plausible a model should consider the discrete (particulate) nature of organisms, which has a high variability at<br />
low dose. It should also be based on the concept of infection from one or more “survivors” of initial dose.<br />
Therefore dose response models for QMRA need to be physiologically plausible and be derived from what is known of the general infection process. There are two models which are derived based on these needs for the QMRA dose response relationship, the exponential and beta Poisson models.<br />
<br />
==== Types of Models ====<br />
<br />
'''Exponential Dose Response Model'''<br />
<br />
Assumptions:<br />
*Poisson distribution of organisms among replicated doses (mean number in dose=d).<br />
*One organism is capable of producing an infection if it arrives at an appropriate site.<br />
*Organisms have independent and identical probability (k) of surviving to reach and infect at an appropriate site. Some sources use the letter 'r' instead of 'k' (equation 1). Here we define r=1/k, so the alternative form for equation 1 is <br />
<br />
<br><br />
'''Beta-Poisson Model''' <br><br />
<br />
Assumptions same as the exponential model except:<br />
*Nonconstant survival and infection probabilities<br />
*Survival probabilities (k) are given by the beta distribution<br />
<br />
The slope of the beta-Poisson dose response curve is more shallow than the exponential. The exponential model is the same as the beta-Poisson model when alpha approaches infinity. The parameters are alpha and N50. N50 is the dose at which 50% of the population is expected to be affected. The beta-Poisson model is sometimes expressed with a beta parameter instead of an N50 parameter; N50=beta*[2^(1/alpha)-1]. Both the alpha and the beta parameters derive from the use of the beta distribution to model nonconstant pathogen survival probabilities.<br />
<br />
The exact form of the beta-Poisson model uses the confluent hypergeometric function, which can be difficult to optimize. However since both the exact and approximate form of the beta Poisson dose response models demonstrate linearity in the low dose range, and there is not a substantial difference between the forms in fitting dose response data, there is not reason to not use the more intuitive form of the beta Poisson. Equation 2 shows the approximate form of the beta Poisson using the N<sub>50</sub> parameter, which can be directly optimized using dose response data or estimated using the conversion in equation 4. <br />
<br />
[[File:Revised_exponential.png|thumb|left|400px]] [[File:beta_poisson_with_beta_beta_conversion.png|thumb|center|400px]]<br /><br />
<br />
== Utility of Using Dose Response Models ==<br />
<br />
An optimized dose response model allows for greater flexibility and a wider range of understanding in the estimated risk. Rather than having a median infectious or lethal dose for a pathogen a model that can describe the full range of probability of response beyond just the median and one that is still accurate at low doses.<br />
<br />
<br />
== Available Dose Response Models ==<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|'''Microbial Group''' ||'''Pathogen'''||'''Recommended Model'''||'''Dose Response Data Reference'''<br />
|-<br />
| rowspan = "13" | '''Bacteria'''<br />
| [[Dose response models for Bacillus anthracis|''Bacillus anthracis'' - Anthrax]]<br />
| Exponential, k = 1.62 E-05, LD<sub>50</sub> = 42,924, Guinea pig, Inhalation, Death<br />
| Druett et al., (1953) <br />
|-<br />
| [[Dose response models for Burkholderia|''Burkholderia'' - Glanders]]<br />
| Exponential, k = 1.0 E-04, ID<sub>50</sub> = 6,919, C57BL6 mice/KHW, Intranasal, Infection <br />
| Brett and Woods (2000)<br />
|-<br />
| [[Dose response models for Campylobacter|''Campylobacter'' - Campylobacteriosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Coxiella burnetii|''Coxiella burnetii'' - Q-fever]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Escherichia coli|''Escherichia coli'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for enterohemorrhagic Escherichia coli (EHEC)|enterohemorrhagic ''Escherichia coli'' (EHEC)]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Francisella tularensis|''Francisella tularensis'' - Tularemia]]<br />
| Exponential, k = 0.047, LD<sub>50</sub> = 14.65, monkeys / SCHU S-4, inhalation, Death<br />
| Day and Berendt, (1972)<br />
|-<br />
| [[Dose response models for Legionella|''Legionella pneumophila'' - Legionella]]<br />
| Exponential, k = 0.06, ID<sub>50</sub> = 11.57, Guinea pigs / Philadelphia 1 strain, Inhalation, Infection<br />
| Muller et al (1983) <br />
|-<br />
| [[Dose response models for Rickettsia rickettsi|''Rickettsia rickettsi'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Salmonella|''Salmonella'' - Salmonellosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Shigella|''Shigella'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Vibrio cholera|''Vibrio cholera'' - Cholera]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Yersinia pestis|''Yersinia pestis'' - Plague]]<br />
| Exponential, k = 1.63E-03, LD<sub>50</sub> = 426.08, Mouse/ CO92, Intranasal, Death<br />
| Lathem et al, (2005) <br />
|-<br />
| rowspan = "8" | '''Virus'''<br />
| [[Dose response models for Adenovirus|Adenovirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Echovirus|''Echovirus'']]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Enteroviruses|Enteroviruses]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Influenza|Influenza]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Lassa virus|''Lassa virus'' - Hemmorhagic fevers]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Rhinovirus|''Rhinovirus'' - Common cold ]]<br />
| Beta-Poisson, α = 0.20, N<sub>50</sub> = 9.22, ID<sub>50</sub> = 9.22, humans/ rhinovirus type 14, strain SF 765, oral, Infection<br />
| Hendley et al., (1972)<br />
|-<br />
| [[Dose response models for Rotavirus|Rotavirus]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for SARS|SARS]]<br />
| Exponential, k = 2.46E-03, LD<sub>50</sub> = 281.97, mice/rSARS-CoV and Mice/MHV-1, intranasal, Death<br />
| DeDiego et al., (2008), De Albuquerque et al., (2006) <br />
|-<br />
| rowspan = "3" | '''Protozoa'''<br />
| [[Dose response models for Cryptosporidium|''Cryptosporidium'' - Cryptosporidiosis]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Entamoeba|Entamoeba]]<br />
|<br />
|<br />
|-<br />
| [[Dose response models for Giardia|''Giardia'' - Giardiasis]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Prion'''<br />
| [[Dose response models for Prion|Prion]]<br />
|<br />
|<br />
|-<br />
| rowspan = "1" | '''Amoeba'''<br />
| [[Dose response models for Naegleria|Naegleria]]<br />
| Exponential, k = 3.42E-07, LD<sub>50</sub> = 2.03E+06, mice/Naegleria fowleri LEE strain, intravenous, Death<br />
| Adams et al. (1976), Haggerty and John (1978) <br />
|}<br />
|}<br />
<br />
<br />
<br />
----<br />
Besides dose response assessment, the other major components of microbial risk assessment are [[hazard identification]], [[exposure assessment]], and [[risk characterization]].</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Echovirus:_Dose_Response_Models&diff=2128Echovirus: Dose Response Models2011-09-12T14:19:46Z<p>Yh: /* Summary */</p>
<hr />
<div>==<center>'''''Echovirus'''''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://camrawiki.anr.msu.edu/images/2/2d/Echovirus.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview '''===<br />
<br />
Echoviruses, members of the ''enterovirus'' genus, is a type of RNA virus that were shown to be a frequent cause of simple febrile illnesses and aseptic meningitis. Most infected persons experience no symptoms or have self-limited disease. Deaths and other adverse consequences are rare and limited to patients with severe echovirus encephalitis or to persons with B cell-deficiency syndromes who develop persistent infection (Modlin 1986).<br />
<br />
Human echoviral infection occurs via fecal-oral transmission. Infants are particularly susceptible to echovirus infection. Both vertical transmission from an infected mother and nosocomial transmission via hospital personnel appear to be important sources of infection for the neonate. Severe disease and death may follow the infection that occurs with the first 10-14 days of life (Modlin 1986).<br />
<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
Schiff et al. (1984) challenged volunteers lacking detectable serum antibody with echovirus-12 in chilled drinking water. The virus used in the study was originally recovered from a child with a clinical diagnosis of erythema infectiousum (fifth disease). The infection with echovirus-12 among volunteers was determined by intestinal shedding of virus and seroconversion.<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ '''Table 2.1. Summary of the echovirus data and best fits'''<br />
| Experiment number || Reference || Host type/pathogen strain || Route/number of doses || Dose units || Response || Best-fit model || Optimized Parameter(s) || LD<sub>50</sub><br />
|-<br />
| 1 (excluding outliners)<br />
| Schiff et al.,1984<br />
| humans/ echovirus-12 strain<br />
| oral/4<br />
| pfu<br />
| infection<br />
| Beta-Poisson<br />
| α = 1.06<br />
N<sub>50</sub> = 921.94<br />
| 921.94<br />
|}<br />
|}<br />
<br />
<br />
----<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_1.xls '''Table 2.2. humans/ echovirus-12 strain''']<br />
| Dose || Infected || Non-infected|| Total<br />
|-<br />
| 330 || 15 || 35 || 50<br />
|-<br />
| 1000 || 9 || 11 || 20<br />
|-<br />
| 3300 || 19 || 7 || 26<br />
|-<br />
| 10000 || 12 || 0 || 12<br />
|-<br />
| 33000 || 2 || 2 || 4<br />
|-<br />
| 330000 || 2 || 1 || 3<br />
|-<br />
| colspan = "4" | Schiff et al.,1984.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_1.xls '''Table 2.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 121.79<br />
| rowspan = "2" | 110.07<br />
| 5<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 11.07 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 11.72<br />
| 4<br />
| 9.49 <br /> ''0.0196''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_1.xls '''Table 2.4: Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.37<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 1006.31<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 1006.31<br />
| 379.05<br />
| 530.33<br />
| 599.68<br />
| 1697.50<br />
| 1889.71<br />
| 2405.62<br />
|}<br />
|}<br />
<br />
<br />
[[File:Schiff1984(1)_Echovirus_BPScatter.png|thumb|left|500px|'''Figure 2.1 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters''']][[File:Schiff1984(1)_Echovirus_BPModel.png|thumb|none|500px|'''Figure 2.2 beta Poisson model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 1 (excluding the outliners).'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_2.xls '''Table 2.5: Dose response data''']<br />
| Dose || Infected || Non-infected || Total<br />
|-<br />
| 330 || 15 || 35 || 50<br />
|-<br />
| 1000 || 9 || 11 || 20<br />
|-<br />
| 3300 || 19 || 7 || 26<br />
|-<br />
| 10000 || 12 || 0 || 12<br />
|-<br />
| colspan = "4" | Schiff et al.,1984.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_2.xls '''Table 2.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 7.39<br />
| rowspan = "2" | 4.18<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''0.041''<br />
| 7.81 <br /> ''0.0605''<br />
|-<br />
| Beta Poisson<br />
| 3.21<br />
| 2<br />
| 5.99 <br /> ''0.201''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_2.xls '''Table 2.7: Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 1.06<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 921.94<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 921.94<br />
| 477.12<br />
| 569.23<br />
| 616.28<br />
| 1378.01<br />
| 1489.05<br />
| 1693.95<br />
|}<br />
|}<br />
<br />
<br />
[[File:Schiff1984(2)_Echovirus_BPScatter.png|thumb|left|500px|'''Figure 2.3 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Schiff1984(2)_Echovirus_BPModel.png|thumb|none|500px|'''Figure 2.4 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
The limited numbers of subjects under dose 33000 and 330000 might have contributed to their deviations from other dose groups and the unsuccessful fitting. By excluding these two outliner groups, it can be seen that significantly better fit was achieved (The reduction of deviance is substantially greater than the chi-square value at the degree of freedom of 2).<br />
<br />
<br />
----<br />
<br />
==='''References'''===<br />
<br />
Modlin, J. F. (1986). "[http://www.jstor.org/stable/4453981 Perinatal Echovirus Infection: Insights from a Literature Review of 61 Cases of SeriousInfection and 16 Outbreaks in Nurseries]." Reviews of Infectious Diseases '''8'''(6): 918-926.<br />
<br />
Schiff, G. M., G. M. Stefanović, E. C. Young, D. S. Sander, J. K. Pennekamp and R. L. Ward (1984). "[http://www.jstor.org/stable/30129376 Studies of echovirus-12 in volunteers: determination of minimal infectious dose and the effect of previous infection on infectious dose]." Journal of Infectious Diseases '''150'''(6): 858-866.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Echovirus:_Dose_Response_Models&diff=2127Echovirus: Dose Response Models2011-09-12T14:12:04Z<p>Yh: /* Optimized Models and Fitting Analyses */</p>
<hr />
<div>==<center>'''''Echovirus'''''</center>==<br />
<br />
<center><big>'''Author: Yin Huang'''</big></center><br />
<center>'''If you want to download this chapter in pdf format, please click [http://camrawiki.anr.msu.edu/images/2/2d/Echovirus.pdf here]''' </center><br />
<center>'''If you want to download the excel spreadsheet of tables, please click the captions of tables. If you want to download a specific figure, just click on the figure'''</center><br />
<br />
<br />
==='''General overview '''===<br />
<br />
Echoviruses, members of the ''enterovirus'' genus, is a type of RNA virus that were shown to be a frequent cause of simple febrile illnesses and aseptic meningitis. Most infected persons experience no symptoms or have self-limited disease. Deaths and other adverse consequences are rare and limited to patients with severe echovirus encephalitis or to persons with B cell-deficiency syndromes who develop persistent infection (Modlin 1986).<br />
<br />
Human echoviral infection occurs via fecal-oral transmission. Infants are particularly susceptible to echovirus infection. Both vertical transmission from an infected mother and nosocomial transmission via hospital personnel appear to be important sources of infection for the neonate. Severe disease and death may follow the infection that occurs with the first 10-14 days of life (Modlin 1986).<br />
<br />
<br />
----<br />
<br />
<br />
==='''Summary Data'''===<br />
<br />
Schiff et al. (1984) challenged volunteers lacking detectable serum antibody with echovirus-12 in chilled drinking water. The virus used in the study was originally recovered from a child with a clinical diagnosis of erythema infectiousum (fifth disease). The infection with echovirus-12 among volunteers was determined by intestinal shedding of virus and seroconversion.<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ '''Table 2.1. Summary of the echovirus data and best fits'''<br />
| Experiment number || Reference || Host type/pathogen strain || Route/number of doses || Dose units || Response || Best-fit model || Optimized Parameter(s) || LD<sub>50</sub><br />
|-<br />
| 1 (excluding outliners)<br />
| Schiff et al.,1984<br />
| humans/ echovirus-12 strain<br />
| oral/4<br />
| pfu<br />
| infection<br />
| Beta-Poisson<br />
| α = 1.06<br />
N<sub>50</sub> = 921.94<br />
| 921.94<br />
|}<br />
|}<br />
<br />
<br />
----<br />
<br />
==='''Optimized Models and Fitting Analyses'''===<br />
<br />
'''Optimization Output for experiment 1'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_1.xls '''Table 2.2. humans/ echovirus-12 strain''']<br />
| Dose || Infected || Non-infected|| Total<br />
|-<br />
| 330 || 15 || 35 || 50<br />
|-<br />
| 1000 || 9 || 11 || 20<br />
|-<br />
| 3300 || 19 || 7 || 26<br />
|-<br />
| 10000 || 12 || 0 || 12<br />
|-<br />
| 33000 || 2 || 2 || 4<br />
|-<br />
| 330000 || 2 || 1 || 3<br />
|-<br />
| colspan = "4" | Schiff et al.,1984.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_1.xls '''Table 2.3: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 121.79<br />
| rowspan = "2" | 110.07<br />
| 5<br />
| rowspan = "2" | 3.84 <br /> ''0''<br />
| 11.07 <br /> ''0''<br />
|-<br />
| Beta Poisson<br />
| 11.72<br />
| 4<br />
| 9.49 <br /> ''0.0196''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_1.xls '''Table 2.4: Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations ''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 0.37<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 1006.31<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 1006.31<br />
| 379.05<br />
| 530.33<br />
| 599.68<br />
| 1697.50<br />
| 1889.71<br />
| 2405.62<br />
|}<br />
|}<br />
<br />
<br />
[[File:Schiff1984(1)_Echovirus_BPScatter.png|thumb|left|500px|'''Figure 2.1 Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters''']][[File:Schiff1984(1)_Echovirus_BPModel.png|thumb|none|500px|'''Figure 2.2 beta Poisson model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
<br />
'''Optimization Output for experiment 1 (excluding the outliners).'''<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_2.xls '''Table 2.5: Dose response data''']<br />
| Dose || Infected || Non-infected || Total<br />
|-<br />
| 330 || 15 || 35 || 50<br />
|-<br />
| 1000 || 9 || 11 || 20<br />
|-<br />
| 3300 || 19 || 7 || 26<br />
|-<br />
| 10000 || 12 || 0 || 12<br />
|-<br />
| colspan = "4" | Schiff et al.,1984.<br />
|}<br />
|}<br />
<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_2.xls '''Table 2.6: Goodness of fit and model selection''']<br />
| Model||Deviance||&Delta;||Degrees <br /> of Freedom||&chi;<sup>2</sup><sub>0.95,1</sub> <br /> ''p-value''||&chi;<sup>2</sup><sub>0.95,m-k</sub> <br /> ''p-value''<br />
|-<br />
| Exponential<br />
| 7.39<br />
| rowspan = "2" | 4.18<br />
| 3<br />
| rowspan = "2" | 3.84 <br /> ''0.041''<br />
| 7.81 <br /> ''0.0605''<br />
|-<br />
| Beta Poisson<br />
| 3.21<br />
| 2<br />
| 5.99 <br /> ''0.201''<br />
|-<br />
| colspan = "6" | '''Beta Poisson is best fitting model'''<br />
|}<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://camrawiki.anr.msu.edu/index.php?title=File:Schiff1984_Echovirus_datafitconf_2.xls '''Table 2.7: Optimized parameters for the best fitting (beta Poisson), obtained from 10,000 bootstrap iterations''']<br />
| rowspan = "2" | Parameter or value<br />
| rowspan = "2" | MLE Estimate<br />
| colspan = "6" | Percentiles<br />
|-<br />
| 0.50%||2.5%||5%||95%||97.5%||99.5%<br />
|-<br />
| &alpha;<br />
| 1.06<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| N<sub>50</sub><br />
| 921.94<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
| --<br />
|-<br />
| LD<sub>50</sub><br />
| 921.94<br />
| 477.12<br />
| 569.23<br />
| 616.28<br />
| 1378.01<br />
| 1489.05<br />
| 1693.95<br />
|}<br />
|}<br />
<br />
<br />
[[File:Schiff1984(2)_Echovirus_BPScatter.png|thumb|left|500px|'''Figure 2.3 Parameter histogram for exponential model (uncertainty of the parameter)''']][[File:Schiff1984(2)_Echovirus_BPModel.png|thumb|none|500px|'''Figure 2.4 Exponential model plot, with confidence bounds around optimized model''']]<br />
<br />
<br />
----<br />
<br />
==='''Summary'''===<br />
<br />
The limited numbers of subjects under dose 33000 and 330000 might have contributed to their deviations from other dose groups and the unsuccessful fitting in section 19.3.1. By excluding these two outliner groups, it can be seen that significantly better fit was achieved in section 19.3.2.<br />
<br />
<br />
----<br />
<br />
<br />
==='''References'''===<br />
<br />
Modlin, J. F. (1986). "[http://www.jstor.org/stable/4453981 Perinatal Echovirus Infection: Insights from a Literature Review of 61 Cases of SeriousInfection and 16 Outbreaks in Nurseries]." Reviews of Infectious Diseases '''8'''(6): 918-926.<br />
<br />
Schiff, G. M., G. M. Stefanović, E. C. Young, D. S. Sander, J. K. Pennekamp and R. L. Ward (1984). "[http://www.jstor.org/stable/30129376 Studies of echovirus-12 in volunteers: determination of minimal infectious dose and the effect of previous infection on infectious dose]." Journal of Infectious Diseases '''150'''(6): 858-866.</div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Exposure_assessment&diff=2089Exposure assessment2011-08-23T20:54:30Z<p>Yh: /* Fecal Output */</p>
<hr />
<div>== Introduction ==<br />
<br />
Exposure at the simplest level is the dose of the pathogen that an individual ingests (eg. number of noroviruses), inhales (eg. numbers of bacteria cells of ''Legionella''), or comes in contact with (eg. numbers of skin bacterial pathogens such as ''Staphylococcus''). This number feeds into the dose-response models to predict the probability of infection. However exposure assessment is very complex and involves a combination of addressing the methods used to measure the microbes and the concentrations in the water or air for example, as well as the timing of the exposure. In most cases exposure can be viewed as a pathway from the source of the pathogen (eg shedding of pathogens by infected individuals, or concentrations in sewage) to the actual exposure (swimming at the beach). This also involves understanding the transport and survival of the microbe. <br /><br />
<br />
<br />
Exposure is often venue or media specific. Federal agencies such as EPA and FDA have developed long term programs to collect and accrue large amounts of data on populations exposures to drinking water and types of food often separated by age. [http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=209866 EPA Exposure Factors Handbook] <br /><br />
<br />
<br />
This section will begin to collect and present the parameters which are associated with exposures for water and fomites and will include pathogen specific parameters. <br />
* Pathogen excretion from infected individuals or populations<br />
* Pathogen occurrence and concentrations in various sources<br />
* Pathogen inactivation over time on various surfaces, in water<br />
* Amount of air inhaled, water ingested<br />
<br />
<br />
== Exposure Pathways ==<br />
<br />
=== [[Drinking Water|Drinking Water]] ===<br />
<br />
: [[Distribution Systems|'''Distribution Systems''']]<br />
<br />
=== [[Recreational Water|Recreational Water]] ===<br />
<br />
=== [[Fomites|Fomites]] ===<br />
<br />
Fomites are any nonliving surface that can harbor a pathogen. Fomites are experienced and encountered everyday and are typically more commonly encountered than other exposure routes, such as drinking water. When developing a QMRA for fomites, there are a number of key pieces of information and data that need to be addressed. First the pathogen survival on fomites must be addressed, this available data that has been used in past QMRAs by CAMRA and non CAMRA investigators. In the [[Fomites|fomite]] page the following parameters are summarized and described:<br />
<br />
* Survival of pathogens on fomites are as decay rates of the pathogens, this does not include chemical inactivation of the pathogens. This data is summarized for Category A agents at this moment.<br />
* Transfer efficiency from fomite to hands, in terms of percent of pathogens transferred from a fomite to a finger.<br />
* Transfer efficiency from hands to mouth, in terms of percent of microorganisms transferred from human fingers to the mouth.<br />
* Contact rates for humans, where the number of hand-to-face behavior is quantified<br />
<br />
<br />
=== [[Indoor Air|Indoor Air]] ===<br />
<br />
=== [[Outdoor Air|Outdoor Air]] ===<br />
<br />
=== [[Droplet Spray|Droplet Spray]] ===<br />
<br />
<br />
== Human Specific Parameters ==<br />
<br />
=== [[Fecal Output|Fecal Output]] ===<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fecal_output_for_different_populations.xls '''Table 1: Fecal Output for Different Populations''']<br />
| '''Population''' || '''Age''' || '''Number of Subjects'''|| '''Mean''' || '''Standard Deviation''' || '''References'''<br />
|-<br />
| colspan = "6" | Healthy people<br />
|-<br />
| Healthy Nigerian Children || 6 months to 5 years || 410 || 109.3 mL/day || 54.07 || Akinbami et al. 1995<br />
|-<br />
| Healthy Nigerian Adult || 23-28 years || 37 || 143.3 g/day || 48.5 || Ogunbiyi 1978<br />
|-<br />
| Healthy British Children and Adult || 11-56 years || 17 || 153 g/day || 79 || Davies et al. 1986<br />
|-<br />
| Healthy American Adult || Adult || 115 || 123.6 g/day || 40.2 || Rendtorff et al. 1967<br />
|-<br />
| colspan = "6" | Ill people<br />
|-<br />
| Indian Children with diarrhea<sup>a</sup> || 3 to 24 months || 70 || 126 g/kilogram of body weight/hour <br />during initial 24 hours and rehydration period || 33.6 || Dutta et al. 2000<br />
|-<br />
| Tunisian Adult with acute diarrhea<sup>b</sup> || over 18 years || 70 || 499 g/day || 284 || Hamza et al. 1999<br />
|-<br />
| American Adult infected with Enteropathogenic Escherichia coli || 18-40 years || 11 || 406 g/day || 300 || Donnenberg 1993<br />
|}<br />
|}<br />
<sup>a</sup> In this study, a high isolation rate of different enteropathogens, e.g. ''Shigella flexneri'', ''Shigella boydii'',''Salmonella typhimurium'', Enteroagrregative ''E. coli'', Enteropathogenic ''E. coli'', ''Aeromonus sp.'' and ''rotavirus'' was observed. <br /><br />
<sup>b</sup> ''Shigella'', ''E. coli'', ''Proteus'', ''Pseudomonas aeruginosa'' were identified from the subjects. <br /><br />
<br />
Akinbami, F., Erinoso, O. and Akinwolere, O. (1995) Defaecation pattern and intestinal transit in nigerian children. African Journal of Medicine and Medical Sciences 24, 337-341. [http://www.ncbi.nlm.nih.gov/pubmed/8886147 Abstract] <br /><br />
<br />
Dutta, P., Dutta, S., Manna, B., Chatterjee, M. and De, A. (2000) Hypo-osmolar oral rehydration salts solution in dehydrating persistent diarrhoea in children: Double-blind, randomized, controlled clinical trial. Acta Paediatrica 89, 411-416. [http://onlinelibrary.wiley.com/doi/10.1111/j.1651-2227.2000.tb00078.x/pdf Full text]<br /><br />
<br />
Hamza, H., Ben Khalifa, H., Baumer, P., Berard, H. and Lecomte, J.M. (1999) Racecadotril versus placebo in the treatment of acute diarrhoea in adults. Alimentary Pharmacology and Therapeutics 13, 15-19. [http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2036.1999.00002.x-i1/pdf Full text] <br /><br />
<br />
Ogunbiyi, T.A. (1978) Whole-gut transit rates and wet stool weight in an urban Nigerian population. World Journal of Surgery 2(3), 387-392. [http://www.springerlink.com/content/v6766540524h765l/fulltext.pdf Full text] <br /><br />
<br />
==== Pathogen Excretion Rate ====<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Concentration_and_incidence_of_microbes.xls '''Table 2. Concentration of microbes in stools of an infected person and incidence of microbes in the United States''']<br />
| '''Organism''' || '''Concentration in stool per gram'''||'''Reference'''||'''Incidence(%)'''||'''Notes'''||'''Reference'''<br />
|-<br />
| rowspan = "3" | ''Giardia''<br />
| rowspan = "3" | 1-5*10<sup>6</sup> <br />
| rowspan = "3" | Jakubowski, 1984<br />
| 3.8<br />
| All age groups<br />
| Howell and Waldron, 1978<br />
|-<br />
| 18-26<br />
| Children in day care centers<br />
| Sealy and Shuman, 1983<br />
|-<br />
| 29-54<br />
| Developed countries<br />
| Black et al, 1977<br />
|-<br />
| rowspan = "2" | ''Cryptosporidium''<br />
| rowspan = "2" | 10<sup>6</sup>-10<sup>7</sup><br />
| rowspan = "2" | Robertson, 1994<br />
| 0.6-20<br />
| Children in day care centers<br />
| Soave and Weikel, 1990<br />
|-<br />
| 27-50<br />
| <br />
| Ungar, 1990<br />
|-<br />
| rowspan = "2" | Hepatitis A<br />
| rowspan = "2" | 10<sup>8</sup><br />
| rowspan = "2" | Coulepis, 1980<br />
| 0.0097<br />
| Reported cases of clinical illness<br />
| MMWR, 1988<br />
|-<br />
| 8.2<br />
| Occurrence of virus in stools of healthy person<br />
| DeFilippes et al, 1987<br />
|-<br />
| rowspan = "2" | Rotavirus<br />
| rowspan = "2" | 10<sup>10</sup>-10<sup>12</sup><br />
| rowspan = "2" | Flewett, 1982<br />
| 10.4<br />
| Annual rates of clinical infection<br />
| Monto et al, 1983<br />
|-<br />
| 29<br />
| Children under 2 years of age<br />
| Champsaur et al, 1984<br />
|-<br />
| Poliovirus<br />
| 10<sup>3</sup>-10<sup>6.5</sup><br />
| Melnick and Rennick, 1980<br />
|<br />
|<br />
|<br />
|-<br />
| Coxsackie and echo virus<br />
| 10<sup>2</sup>-10<sup>5.5</sup>(max. 10<sup>7.2</sup>)<br />
| Melnick and Rennick, 1980<br />
|<br />
|<br />
|<br />
|-<br />
| Fecal coliform<br />
| 10<sup>7</sup>-10<sup>9</sup><br />
| Feachem et al, 1983<br />
|<br />
|<br />
|<br />
|-<br />
| rowspan = "2"| Enterovirus<br />
| rowspan = "2"|<br />
| rowspan = "2"|<br />
| 10<br />
| Occurrence in fecally soiled diapers<br />
| Peterson, 1972; 1974<br />
|-<br />
| 30-40<br />
| During the summer months<br />
| Fox and Hall, 1980<br />
|}<br />
|}<br />
<br />
Black R.E., Dykes A.C, Sinclair S.A., and Wells J.G. (1977) Giardiasis in day-care centers: evidence of person to person transmission. Pediatrics. 60: 193-197. [http://pediatrics.aappublications.org/content/60/4/486.full.pdf+html Full text]<br />
<br />
Champsaur H. Questiaux E., Prevot J. et al. (1984) Rotavirus carriage, asymptomatic infection, and disease in the first years of life. I. Virus shedding. J Infect Dis. 149: 667-674. [http://jid.oxfordjournals.org/content/149/5/667.full.pdf+html Full text]<br />
<br />
Coulepis A.G., Locarnini S.A., Westway E.G., Tannock G.A., Gust I.D. Biophysical and biochemical characterization of hepatitis A virus. J Infect Dis 141: 151-156. [http://content.karger.com/ProdukteDB/produkte.asp?Doi=149314 Summary]<br />
<br />
Feachem R.G., Bradley D.J., Garelick H. , Mara D.D.. 1983. Sanitation and disease (John Wiley and Sons, Inc. NY)<br />
<br />
Flewett T.H.. Clinical features of rotavirus infections, in virus infections of the gastrointestinal tract, edited by D.A.J. Tyrell, A.Z. Kapikian (Marcel Dekker Inc. NY, 1982). 125-146.<br />
<br />
Fox J.P., Hall, C.E. Viruses in families(PSG Publishing Company, Inc. Littleton, MA. 1980).<br />
<br />
Howell R.T.k, Waldron B.S.. Intestinal parasites in Arkansas. J Arkansas Med Soc. 75: 212-214.<br />
<br />
Jakubowski W. Detection of Giardia cysts in drinking water: State-of-the-art, in Giardia and Giardiasis, Biology, Pathogenesis and Epidemiology, (Plenum Press, NY, 1984). 263-285.<br />
<br />
Melnick J.L. (1957) Special publication of the New York Acad of Science. 5: 365-381.<br />
<br />
Melnick J.L., Rennick V. (1980) Infectivity of enterovirus as found in human stools. J Med Virology. 5: 205-220. [http://onlinelibrary.wiley.com/doi/10.1002/jmv.1890050305/pdf Full text]<br />
<br />
Monto A.S., Koopman J.S. Longini I.M., Isaacson R.E.. (1983) The Tecumseh study XII. Enteric agents in the community, 1976-1981. J Infect Dis. 148: 284-291. [http://jid.oxfordjournals.org/content/148/2/284.full.pdf+html Full text]<br />
<br />
MMWR. (1988) Morbidity and mortality weekly, summary-cases of specific notifiable diseases, United States. MMWR. 36: 840<br />
<br />
Peterson M.L. (1972) The ocurrence and survival of viruses in municipal solid waste. Dissertation abstracts. 33/3, 2232-B-2233-B. Ann Arbor, MI.<br />
<br />
Peterson M.L. (1974) Soiled disposable diapers: A potential source of viruses. Amer J Public Hlth. 64: 912-914. [http://ajph.aphapublications.org/cgi/reprint/64/9/912.pdf Full text]<br />
<br />
Robertson L.J., Smith H.V., Paton C.A.. Occurrence of Giardia and Cryptospordidium oocysts in sewage effluent in six sewage plants in Scotland and the prevalence of cryptosporidiosis and giardiasis diagnosed in the communities served by those plants, in Protozoan Parasites in Water.. 45-49<br />
<br />
Sealy D.P, Shuman S.H. (1983) Endemic giardiasis and day care. Pediatrics. 72: 154-158. [http://pediatrics.aappublications.org/content/72/2/154.full.pdf+html Full text]<br />
<br />
Soave R., Weikel C.S.. Cryptosporidum and other protozoa including Isospora, Sacryocysts, Blantidium coli and Blastocysts, in Principles and Practice of Infectious Disease. (Churchill Livingstone Inc., NY, 1990) 2122-2130<br />
<br />
Ungar B.L. Cryptosporidiosis in humans (homo sapiens), in Cryptosporidiosis in man and animals. (CRC Press, Boca Raton, FL, 1990). 59-82<br />
<br />
=== [[Ingestion during Swimming|Ingestion during Swimming]] ===<br />
<br />
''' [http://wiki.camra.msu.edu/index.php?title=File:Ingestion_during_swimming.xls Table 1. Water Ingestion by selected groups of swimmers]'''<br />
<br />
Original survey data can be obtained by [http://wiki.camra.msu.edu/index.php?title=File:Ingestion_Results_Pilot_Study-2.xls this link]. <br /><br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
| '''Population''' || '''Age'''|| ''' Number of Subjects '''|| '''Average water ingestion (mL/45 minute)'''||'''Standard deviation'''<br />
|-<br />
| United States Adults<br />
| Older than 18 years old<br />
| 12<br />
| 16 <br />
| 19<br />
|-<br />
| United States Non-adults<br />
| 18 years old and younger<br />
| 41<br />
| 37<br />
| 31<br />
|}<br />
|}<br />
a: Non-parametric Wilcoxon Rank Sum test was applied for statistical analysis. <br /> b: The comparison between adults and non-adults are significantly different at 5% level. The comparison between different gender groups was evaluated and not significant.<br /> Dufour A, Evans O, Behymer T and Cantu R. (2006) Water ingestion during swimming activities in a pool: A pilot study. Journal of Water Health. 04.4: 425-430. [http://www.pwtag.org/researchdocs/Used%20Ref%20docs/14%20Water%20Ingetion%20study%20%28Dufour%29.pdf Full text]<br />
<br />
<br />
<br />
=== Respiration ===<br />
<br />
==== Pathogen Excretion Rate ====<br />
<br />
=== [[Drinking Water|Drinking Water]] ===<br />
<br />
=== Human Fomite Interaction ===<br />
<br />
=== Droplet Spray ===<br />
<br />
<br />
<br />
----<br />
<br />
<big> Besides exposure assessment, the other major components of microbial risk assessment are [[hazard identification]], [[dose response assessment]], and [[risk characterization]] </big></div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Exposure_assessment&diff=2088Exposure assessment2011-08-23T20:54:02Z<p>Yh: /* Fecal Output */</p>
<hr />
<div>== Introduction ==<br />
<br />
Exposure at the simplest level is the dose of the pathogen that an individual ingests (eg. number of noroviruses), inhales (eg. numbers of bacteria cells of ''Legionella''), or comes in contact with (eg. numbers of skin bacterial pathogens such as ''Staphylococcus''). This number feeds into the dose-response models to predict the probability of infection. However exposure assessment is very complex and involves a combination of addressing the methods used to measure the microbes and the concentrations in the water or air for example, as well as the timing of the exposure. In most cases exposure can be viewed as a pathway from the source of the pathogen (eg shedding of pathogens by infected individuals, or concentrations in sewage) to the actual exposure (swimming at the beach). This also involves understanding the transport and survival of the microbe. <br /><br />
<br />
<br />
Exposure is often venue or media specific. Federal agencies such as EPA and FDA have developed long term programs to collect and accrue large amounts of data on populations exposures to drinking water and types of food often separated by age. [http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=209866 EPA Exposure Factors Handbook] <br /><br />
<br />
<br />
This section will begin to collect and present the parameters which are associated with exposures for water and fomites and will include pathogen specific parameters. <br />
* Pathogen excretion from infected individuals or populations<br />
* Pathogen occurrence and concentrations in various sources<br />
* Pathogen inactivation over time on various surfaces, in water<br />
* Amount of air inhaled, water ingested<br />
<br />
<br />
== Exposure Pathways ==<br />
<br />
=== [[Drinking Water|Drinking Water]] ===<br />
<br />
: [[Distribution Systems|'''Distribution Systems''']]<br />
<br />
=== [[Recreational Water|Recreational Water]] ===<br />
<br />
=== [[Fomites|Fomites]] ===<br />
<br />
Fomites are any nonliving surface that can harbor a pathogen. Fomites are experienced and encountered everyday and are typically more commonly encountered than other exposure routes, such as drinking water. When developing a QMRA for fomites, there are a number of key pieces of information and data that need to be addressed. First the pathogen survival on fomites must be addressed, this available data that has been used in past QMRAs by CAMRA and non CAMRA investigators. In the [[Fomites|fomite]] page the following parameters are summarized and described:<br />
<br />
* Survival of pathogens on fomites are as decay rates of the pathogens, this does not include chemical inactivation of the pathogens. This data is summarized for Category A agents at this moment.<br />
* Transfer efficiency from fomite to hands, in terms of percent of pathogens transferred from a fomite to a finger.<br />
* Transfer efficiency from hands to mouth, in terms of percent of microorganisms transferred from human fingers to the mouth.<br />
* Contact rates for humans, where the number of hand-to-face behavior is quantified<br />
<br />
<br />
=== [[Indoor Air|Indoor Air]] ===<br />
<br />
=== [[Outdoor Air|Outdoor Air]] ===<br />
<br />
=== [[Droplet Spray|Droplet Spray]] ===<br />
<br />
<br />
== Human Specific Parameters ==<br />
<br />
=== [[Fecal Output|Fecal Output]] ===<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fecal_output_for_different_populations.xls '''Table 1: Fecal Output for Different Populations''']<br />
| '''Population''' || '''Age''' || '''Number of Subjects'''|| '''Mean''' || '''Standard Deviation''' || '''References'''<br />
|-<br />
| colspan = "6" | Healthy people<br />
|-<br />
| Healthy Nigerian Children || 6 months to 5 years || 410 || 109.3 mL/day || 54.07 || Akinbami et al. 1995<br />
|-<br />
| Healthy Nigerian Adult || 23-28 years || 37 || 143.3 g/day || 48.5 || Ogunbiyi 1978<br />
|-<br />
| Healthy British Children and Adult || 11-56 years || 17 || 153 g/day || 79 || Davies et al. 1986<br />
|-<br />
| Healthy American Adult || Adult || 115 || 123.6 g/day || 40.2 || Rendtorff et al. 1967<br />
|-<br />
| colspan = "6" | Ill people<br />
|-<br />
| Indian Children with diarrhea<sup>a</sup> || 3 to 24 months || 70 || 126 g/kilogram of body weight/hour <br />during initial 24 hours and rehydration period || 33.6 || Dutta et al. 2000<br />
|-<br />
| Tunisian Adult with acute diarrhea<sup>b</sup> || over 18 years || 70 || 499 g/day || 284 || Hamza et al. 1999<br />
|-<br />
| American Adult infected with Enteropathogenic Escherichia coli || 18-40 years || 11 || 406 g/day || 300 || Donnenberg 1993<br />
|}<br />
|}<br />
<sup>a</sup> In this study, a high isolation rate of different enteropathogens, e.g. ''Shigella flexneri'', ''Shigella boydii'',''Salmonella typhimurium'', Enteroagrregative ''E. coli'', Enteropathogenic ''E. coli'', ''Aeromonus sp.'' and ''rotavirus'' was observed. <br /><br />
<sup>b</sup> ''Shigella'', ''E. coli'', ''Proteus'', ''Pseudomonas aeruginosa'' were identified from the subjects. <br /><br />
<br />
Akinbami, F., Erinoso, O. and Akinwolere, O. (1995) Defaecation pattern and intestinal transit in nigerian children. African Journal of Medicine and Medical Sciences 24, 337-341. [http://www.ncbi.nlm.nih.gov/pubmed/8886147 Abstract] <br /><br />
<br />
Dutta, P., Dutta, S., Manna, B., Chatterjee, M. and De, A. (2000) Hypo-osmolar oral rehydration salts solution in dehydrating persistent diarrhoea in children: Double-blind, randomized, controlled clinical trial. Acta Paediatrica 89, 411-416. [http://onlinelibrary.wiley.com/doi/10.1111/j.1651-2227.2000.tb00078.x/pdf Full text]<br /><br />
<br />
Hamza, H., Ben Khalifa, H., Baumer, P., Berard, H. and Lecomte, J.M. (1999) Racecadotril versus placebo in the treatment of acute diarrhoea in adults. Alimentary Pharmacology and Therapeutics 13, 15-19. [http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2036.1999.00002.x-i1/pdf Full text] <br /><br />
<br />
Ogunbiyi, T.A. (1978) Whole-gut transit rates and wet stool weight in an urban Nigerian population. World Journal of Surgery 2(3), 387-392. [http://www.springerlink.com/content/v6766540524h765l/fulltext.pdf full text] <br /><br />
<br />
==== Pathogen Excretion Rate ====<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Concentration_and_incidence_of_microbes.xls '''Table 2. Concentration of microbes in stools of an infected person and incidence of microbes in the United States''']<br />
| '''Organism''' || '''Concentration in stool per gram'''||'''Reference'''||'''Incidence(%)'''||'''Notes'''||'''Reference'''<br />
|-<br />
| rowspan = "3" | ''Giardia''<br />
| rowspan = "3" | 1-5*10<sup>6</sup> <br />
| rowspan = "3" | Jakubowski, 1984<br />
| 3.8<br />
| All age groups<br />
| Howell and Waldron, 1978<br />
|-<br />
| 18-26<br />
| Children in day care centers<br />
| Sealy and Shuman, 1983<br />
|-<br />
| 29-54<br />
| Developed countries<br />
| Black et al, 1977<br />
|-<br />
| rowspan = "2" | ''Cryptosporidium''<br />
| rowspan = "2" | 10<sup>6</sup>-10<sup>7</sup><br />
| rowspan = "2" | Robertson, 1994<br />
| 0.6-20<br />
| Children in day care centers<br />
| Soave and Weikel, 1990<br />
|-<br />
| 27-50<br />
| <br />
| Ungar, 1990<br />
|-<br />
| rowspan = "2" | Hepatitis A<br />
| rowspan = "2" | 10<sup>8</sup><br />
| rowspan = "2" | Coulepis, 1980<br />
| 0.0097<br />
| Reported cases of clinical illness<br />
| MMWR, 1988<br />
|-<br />
| 8.2<br />
| Occurrence of virus in stools of healthy person<br />
| DeFilippes et al, 1987<br />
|-<br />
| rowspan = "2" | Rotavirus<br />
| rowspan = "2" | 10<sup>10</sup>-10<sup>12</sup><br />
| rowspan = "2" | Flewett, 1982<br />
| 10.4<br />
| Annual rates of clinical infection<br />
| Monto et al, 1983<br />
|-<br />
| 29<br />
| Children under 2 years of age<br />
| Champsaur et al, 1984<br />
|-<br />
| Poliovirus<br />
| 10<sup>3</sup>-10<sup>6.5</sup><br />
| Melnick and Rennick, 1980<br />
|<br />
|<br />
|<br />
|-<br />
| Coxsackie and echo virus<br />
| 10<sup>2</sup>-10<sup>5.5</sup>(max. 10<sup>7.2</sup>)<br />
| Melnick and Rennick, 1980<br />
|<br />
|<br />
|<br />
|-<br />
| Fecal coliform<br />
| 10<sup>7</sup>-10<sup>9</sup><br />
| Feachem et al, 1983<br />
|<br />
|<br />
|<br />
|-<br />
| rowspan = "2"| Enterovirus<br />
| rowspan = "2"|<br />
| rowspan = "2"|<br />
| 10<br />
| Occurrence in fecally soiled diapers<br />
| Peterson, 1972; 1974<br />
|-<br />
| 30-40<br />
| During the summer months<br />
| Fox and Hall, 1980<br />
|}<br />
|}<br />
<br />
Black R.E., Dykes A.C, Sinclair S.A., and Wells J.G. (1977) Giardiasis in day-care centers: evidence of person to person transmission. Pediatrics. 60: 193-197. [http://pediatrics.aappublications.org/content/60/4/486.full.pdf+html Full text]<br />
<br />
Champsaur H. Questiaux E., Prevot J. et al. (1984) Rotavirus carriage, asymptomatic infection, and disease in the first years of life. I. Virus shedding. J Infect Dis. 149: 667-674. [http://jid.oxfordjournals.org/content/149/5/667.full.pdf+html Full text]<br />
<br />
Coulepis A.G., Locarnini S.A., Westway E.G., Tannock G.A., Gust I.D. Biophysical and biochemical characterization of hepatitis A virus. J Infect Dis 141: 151-156. [http://content.karger.com/ProdukteDB/produkte.asp?Doi=149314 Summary]<br />
<br />
Feachem R.G., Bradley D.J., Garelick H. , Mara D.D.. 1983. Sanitation and disease (John Wiley and Sons, Inc. NY)<br />
<br />
Flewett T.H.. Clinical features of rotavirus infections, in virus infections of the gastrointestinal tract, edited by D.A.J. Tyrell, A.Z. Kapikian (Marcel Dekker Inc. NY, 1982). 125-146.<br />
<br />
Fox J.P., Hall, C.E. Viruses in families(PSG Publishing Company, Inc. Littleton, MA. 1980).<br />
<br />
Howell R.T.k, Waldron B.S.. Intestinal parasites in Arkansas. J Arkansas Med Soc. 75: 212-214.<br />
<br />
Jakubowski W. Detection of Giardia cysts in drinking water: State-of-the-art, in Giardia and Giardiasis, Biology, Pathogenesis and Epidemiology, (Plenum Press, NY, 1984). 263-285.<br />
<br />
Melnick J.L. (1957) Special publication of the New York Acad of Science. 5: 365-381.<br />
<br />
Melnick J.L., Rennick V. (1980) Infectivity of enterovirus as found in human stools. J Med Virology. 5: 205-220. [http://onlinelibrary.wiley.com/doi/10.1002/jmv.1890050305/pdf Full text]<br />
<br />
Monto A.S., Koopman J.S. Longini I.M., Isaacson R.E.. (1983) The Tecumseh study XII. Enteric agents in the community, 1976-1981. J Infect Dis. 148: 284-291. [http://jid.oxfordjournals.org/content/148/2/284.full.pdf+html Full text]<br />
<br />
MMWR. (1988) Morbidity and mortality weekly, summary-cases of specific notifiable diseases, United States. MMWR. 36: 840<br />
<br />
Peterson M.L. (1972) The ocurrence and survival of viruses in municipal solid waste. Dissertation abstracts. 33/3, 2232-B-2233-B. Ann Arbor, MI.<br />
<br />
Peterson M.L. (1974) Soiled disposable diapers: A potential source of viruses. Amer J Public Hlth. 64: 912-914. [http://ajph.aphapublications.org/cgi/reprint/64/9/912.pdf Full text]<br />
<br />
Robertson L.J., Smith H.V., Paton C.A.. Occurrence of Giardia and Cryptospordidium oocysts in sewage effluent in six sewage plants in Scotland and the prevalence of cryptosporidiosis and giardiasis diagnosed in the communities served by those plants, in Protozoan Parasites in Water.. 45-49<br />
<br />
Sealy D.P, Shuman S.H. (1983) Endemic giardiasis and day care. Pediatrics. 72: 154-158. [http://pediatrics.aappublications.org/content/72/2/154.full.pdf+html Full text]<br />
<br />
Soave R., Weikel C.S.. Cryptosporidum and other protozoa including Isospora, Sacryocysts, Blantidium coli and Blastocysts, in Principles and Practice of Infectious Disease. (Churchill Livingstone Inc., NY, 1990) 2122-2130<br />
<br />
Ungar B.L. Cryptosporidiosis in humans (homo sapiens), in Cryptosporidiosis in man and animals. (CRC Press, Boca Raton, FL, 1990). 59-82<br />
<br />
=== [[Ingestion during Swimming|Ingestion during Swimming]] ===<br />
<br />
''' [http://wiki.camra.msu.edu/index.php?title=File:Ingestion_during_swimming.xls Table 1. Water Ingestion by selected groups of swimmers]'''<br />
<br />
Original survey data can be obtained by [http://wiki.camra.msu.edu/index.php?title=File:Ingestion_Results_Pilot_Study-2.xls this link]. <br /><br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
| '''Population''' || '''Age'''|| ''' Number of Subjects '''|| '''Average water ingestion (mL/45 minute)'''||'''Standard deviation'''<br />
|-<br />
| United States Adults<br />
| Older than 18 years old<br />
| 12<br />
| 16 <br />
| 19<br />
|-<br />
| United States Non-adults<br />
| 18 years old and younger<br />
| 41<br />
| 37<br />
| 31<br />
|}<br />
|}<br />
a: Non-parametric Wilcoxon Rank Sum test was applied for statistical analysis. <br /> b: The comparison between adults and non-adults are significantly different at 5% level. The comparison between different gender groups was evaluated and not significant.<br /> Dufour A, Evans O, Behymer T and Cantu R. (2006) Water ingestion during swimming activities in a pool: A pilot study. Journal of Water Health. 04.4: 425-430. [http://www.pwtag.org/researchdocs/Used%20Ref%20docs/14%20Water%20Ingetion%20study%20%28Dufour%29.pdf Full text]<br />
<br />
<br />
<br />
=== Respiration ===<br />
<br />
==== Pathogen Excretion Rate ====<br />
<br />
=== [[Drinking Water|Drinking Water]] ===<br />
<br />
=== Human Fomite Interaction ===<br />
<br />
=== Droplet Spray ===<br />
<br />
<br />
<br />
----<br />
<br />
<big> Besides exposure assessment, the other major components of microbial risk assessment are [[hazard identification]], [[dose response assessment]], and [[risk characterization]] </big></div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Exposure_assessment&diff=2087Exposure assessment2011-08-23T20:52:02Z<p>Yh: /* Fecal Output */</p>
<hr />
<div>== Introduction ==<br />
<br />
Exposure at the simplest level is the dose of the pathogen that an individual ingests (eg. number of noroviruses), inhales (eg. numbers of bacteria cells of ''Legionella''), or comes in contact with (eg. numbers of skin bacterial pathogens such as ''Staphylococcus''). This number feeds into the dose-response models to predict the probability of infection. However exposure assessment is very complex and involves a combination of addressing the methods used to measure the microbes and the concentrations in the water or air for example, as well as the timing of the exposure. In most cases exposure can be viewed as a pathway from the source of the pathogen (eg shedding of pathogens by infected individuals, or concentrations in sewage) to the actual exposure (swimming at the beach). This also involves understanding the transport and survival of the microbe. <br /><br />
<br />
<br />
Exposure is often venue or media specific. Federal agencies such as EPA and FDA have developed long term programs to collect and accrue large amounts of data on populations exposures to drinking water and types of food often separated by age. [http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=209866 EPA Exposure Factors Handbook] <br /><br />
<br />
<br />
This section will begin to collect and present the parameters which are associated with exposures for water and fomites and will include pathogen specific parameters. <br />
* Pathogen excretion from infected individuals or populations<br />
* Pathogen occurrence and concentrations in various sources<br />
* Pathogen inactivation over time on various surfaces, in water<br />
* Amount of air inhaled, water ingested<br />
<br />
<br />
== Exposure Pathways ==<br />
<br />
=== [[Drinking Water|Drinking Water]] ===<br />
<br />
: [[Distribution Systems|'''Distribution Systems''']]<br />
<br />
=== [[Recreational Water|Recreational Water]] ===<br />
<br />
=== [[Fomites|Fomites]] ===<br />
<br />
Fomites are any nonliving surface that can harbor a pathogen. Fomites are experienced and encountered everyday and are typically more commonly encountered than other exposure routes, such as drinking water. When developing a QMRA for fomites, there are a number of key pieces of information and data that need to be addressed. First the pathogen survival on fomites must be addressed, this available data that has been used in past QMRAs by CAMRA and non CAMRA investigators. In the [[Fomites|fomite]] page the following parameters are summarized and described:<br />
<br />
* Survival of pathogens on fomites are as decay rates of the pathogens, this does not include chemical inactivation of the pathogens. This data is summarized for Category A agents at this moment.<br />
* Transfer efficiency from fomite to hands, in terms of percent of pathogens transferred from a fomite to a finger.<br />
* Transfer efficiency from hands to mouth, in terms of percent of microorganisms transferred from human fingers to the mouth.<br />
* Contact rates for humans, where the number of hand-to-face behavior is quantified<br />
<br />
<br />
=== [[Indoor Air|Indoor Air]] ===<br />
<br />
=== [[Outdoor Air|Outdoor Air]] ===<br />
<br />
=== [[Droplet Spray|Droplet Spray]] ===<br />
<br />
<br />
== Human Specific Parameters ==<br />
<br />
=== [[Fecal Output|Fecal Output]] ===<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fecal_output_for_different_populations.xls '''Table 1: Fecal Output for Different Populations''']<br />
| '''Population''' || '''Age''' || '''Number of Subjects'''|| '''Mean''' || '''Standard Deviation''' || '''References'''<br />
|-<br />
| colspan = "6" | Healthy people<br />
|-<br />
| Healthy Nigerian Children || 6 months to 5 years || 410 || 109.3 mL/day || 54.07 || Akinbami et al. 1995<br />
|-<br />
| Healthy Nigerian Adult || 23-28 years || 37 || 143.3 g/day || 48.5 || Ogunbiyi 1978<br />
|-<br />
| Healthy British Children and Adult || 11-56 years || 17 || 153 g/day || 79 || Davies et al. 1986<br />
|-<br />
| Healthy American Adult || Adult || 115 || 123.6 g/day || 40.2 || Rendtorff et al. 1967<br />
|-<br />
| colspan = "6" | Ill people<br />
|-<br />
| Indian Children with diarrhea<sup>a</sup> || 3 to 24 months || 70 || 126 g/kilogram of body weight/hour <br />during initial 24 hours and rehydration period || 33.6 || Dutta et al. 2000<br />
|-<br />
| Tunisian Adult with acute diarrhea<sup>b</sup> || over 18 years || 70 || 499 g/day || 284 || Hamza et al. 1999<br />
|-<br />
| American Adult infected with Enteropathogenic Escherichia coli || 18-40 years || 11 || 406 g/day || 300 || Donnenberg 1993<br />
|}<br />
|}<br />
<sup>a</sup> In this study, a high isolation rate of different enteropathogens, e.g. ''Shigella flexneri'', ''Shigella boydii'',''Salmonella typhimurium'', Enteroagrregative ''E. coli'', Enteropathogenic ''E. coli'', ''Aeromonus sp.'' and ''rotavirus'' was observed. <br /><br />
<sup>b</sup> ''Shigella'', ''E. coli'', ''Proteus'', ''Pseudomonas aeruginosa'' were identified from the subjects. <br /><br />
<br />
Akinbami, F., Erinoso, O. and Akinwolere, O. (1995) Defaecation pattern and intestinal transit in nigerian children. African Journal of Medicine and Medical Sciences 24, 337-341. [http://www.ncbi.nlm.nih.gov/pubmed/8886147 Abstract] <br /><br />
<br />
Dutta, P., Dutta, S., Manna, B., Chatterjee, M. and De, A. (2000) Hypo-osmolar oral rehydration salts solution in dehydrating persistent diarrhoea in children: Double-blind, randomized, controlled clinical trial. Acta Paediatrica 89, 411-416. [http://onlinelibrary.wiley.com/doi/10.1111/j.1651-2227.2000.tb00078.x/pdf Full text]<br /><br />
<br />
Hamza, H., Ben Khalifa, H., Baumer, P., Berard, H. and Lecomte, J.M. (1999) Racecadotril versus placebo in the treatment of acute diarrhoea in adults. Alimentary Pharmacology and Therapeutics 13, 15-19. [http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2036.1999.00002.x-i1/pdf Full text] <br /><br />
<br />
Ogunbiyi, T.A. (1978) Whole-gut transit rates and wet stool weight in an urban Nigerian population. World Journal of Surgery 2(3), 387-392. [http://www.springerlink.com/content/v6766540524h765l/fulltext.pdf] <br /><br />
<br />
==== Pathogen Excretion Rate ====<br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Concentration_and_incidence_of_microbes.xls '''Table 2. Concentration of microbes in stools of an infected person and incidence of microbes in the United States''']<br />
| '''Organism''' || '''Concentration in stool per gram'''||'''Reference'''||'''Incidence(%)'''||'''Notes'''||'''Reference'''<br />
|-<br />
| rowspan = "3" | ''Giardia''<br />
| rowspan = "3" | 1-5*10<sup>6</sup> <br />
| rowspan = "3" | Jakubowski, 1984<br />
| 3.8<br />
| All age groups<br />
| Howell and Waldron, 1978<br />
|-<br />
| 18-26<br />
| Children in day care centers<br />
| Sealy and Shuman, 1983<br />
|-<br />
| 29-54<br />
| Developed countries<br />
| Black et al, 1977<br />
|-<br />
| rowspan = "2" | ''Cryptosporidium''<br />
| rowspan = "2" | 10<sup>6</sup>-10<sup>7</sup><br />
| rowspan = "2" | Robertson, 1994<br />
| 0.6-20<br />
| Children in day care centers<br />
| Soave and Weikel, 1990<br />
|-<br />
| 27-50<br />
| <br />
| Ungar, 1990<br />
|-<br />
| rowspan = "2" | Hepatitis A<br />
| rowspan = "2" | 10<sup>8</sup><br />
| rowspan = "2" | Coulepis, 1980<br />
| 0.0097<br />
| Reported cases of clinical illness<br />
| MMWR, 1988<br />
|-<br />
| 8.2<br />
| Occurrence of virus in stools of healthy person<br />
| DeFilippes et al, 1987<br />
|-<br />
| rowspan = "2" | Rotavirus<br />
| rowspan = "2" | 10<sup>10</sup>-10<sup>12</sup><br />
| rowspan = "2" | Flewett, 1982<br />
| 10.4<br />
| Annual rates of clinical infection<br />
| Monto et al, 1983<br />
|-<br />
| 29<br />
| Children under 2 years of age<br />
| Champsaur et al, 1984<br />
|-<br />
| Poliovirus<br />
| 10<sup>3</sup>-10<sup>6.5</sup><br />
| Melnick and Rennick, 1980<br />
|<br />
|<br />
|<br />
|-<br />
| Coxsackie and echo virus<br />
| 10<sup>2</sup>-10<sup>5.5</sup>(max. 10<sup>7.2</sup>)<br />
| Melnick and Rennick, 1980<br />
|<br />
|<br />
|<br />
|-<br />
| Fecal coliform<br />
| 10<sup>7</sup>-10<sup>9</sup><br />
| Feachem et al, 1983<br />
|<br />
|<br />
|<br />
|-<br />
| rowspan = "2"| Enterovirus<br />
| rowspan = "2"|<br />
| rowspan = "2"|<br />
| 10<br />
| Occurrence in fecally soiled diapers<br />
| Peterson, 1972; 1974<br />
|-<br />
| 30-40<br />
| During the summer months<br />
| Fox and Hall, 1980<br />
|}<br />
|}<br />
<br />
Black R.E., Dykes A.C, Sinclair S.A., and Wells J.G. (1977) Giardiasis in day-care centers: evidence of person to person transmission. Pediatrics. 60: 193-197. [http://pediatrics.aappublications.org/content/60/4/486.full.pdf+html Full text]<br />
<br />
Champsaur H. Questiaux E., Prevot J. et al. (1984) Rotavirus carriage, asymptomatic infection, and disease in the first years of life. I. Virus shedding. J Infect Dis. 149: 667-674. [http://jid.oxfordjournals.org/content/149/5/667.full.pdf+html Full text]<br />
<br />
Coulepis A.G., Locarnini S.A., Westway E.G., Tannock G.A., Gust I.D. Biophysical and biochemical characterization of hepatitis A virus. J Infect Dis 141: 151-156. [http://content.karger.com/ProdukteDB/produkte.asp?Doi=149314 Summary]<br />
<br />
Feachem R.G., Bradley D.J., Garelick H. , Mara D.D.. 1983. Sanitation and disease (John Wiley and Sons, Inc. NY)<br />
<br />
Flewett T.H.. Clinical features of rotavirus infections, in virus infections of the gastrointestinal tract, edited by D.A.J. Tyrell, A.Z. Kapikian (Marcel Dekker Inc. NY, 1982). 125-146.<br />
<br />
Fox J.P., Hall, C.E. Viruses in families(PSG Publishing Company, Inc. Littleton, MA. 1980).<br />
<br />
Howell R.T.k, Waldron B.S.. Intestinal parasites in Arkansas. J Arkansas Med Soc. 75: 212-214.<br />
<br />
Jakubowski W. Detection of Giardia cysts in drinking water: State-of-the-art, in Giardia and Giardiasis, Biology, Pathogenesis and Epidemiology, (Plenum Press, NY, 1984). 263-285.<br />
<br />
Melnick J.L. (1957) Special publication of the New York Acad of Science. 5: 365-381.<br />
<br />
Melnick J.L., Rennick V. (1980) Infectivity of enterovirus as found in human stools. J Med Virology. 5: 205-220. [http://onlinelibrary.wiley.com/doi/10.1002/jmv.1890050305/pdf Full text]<br />
<br />
Monto A.S., Koopman J.S. Longini I.M., Isaacson R.E.. (1983) The Tecumseh study XII. Enteric agents in the community, 1976-1981. J Infect Dis. 148: 284-291. [http://jid.oxfordjournals.org/content/148/2/284.full.pdf+html Full text]<br />
<br />
MMWR. (1988) Morbidity and mortality weekly, summary-cases of specific notifiable diseases, United States. MMWR. 36: 840<br />
<br />
Peterson M.L. (1972) The ocurrence and survival of viruses in municipal solid waste. Dissertation abstracts. 33/3, 2232-B-2233-B. Ann Arbor, MI.<br />
<br />
Peterson M.L. (1974) Soiled disposable diapers: A potential source of viruses. Amer J Public Hlth. 64: 912-914. [http://ajph.aphapublications.org/cgi/reprint/64/9/912.pdf Full text]<br />
<br />
Robertson L.J., Smith H.V., Paton C.A.. Occurrence of Giardia and Cryptospordidium oocysts in sewage effluent in six sewage plants in Scotland and the prevalence of cryptosporidiosis and giardiasis diagnosed in the communities served by those plants, in Protozoan Parasites in Water.. 45-49<br />
<br />
Sealy D.P, Shuman S.H. (1983) Endemic giardiasis and day care. Pediatrics. 72: 154-158. [http://pediatrics.aappublications.org/content/72/2/154.full.pdf+html Full text]<br />
<br />
Soave R., Weikel C.S.. Cryptosporidum and other protozoa including Isospora, Sacryocysts, Blantidium coli and Blastocysts, in Principles and Practice of Infectious Disease. (Churchill Livingstone Inc., NY, 1990) 2122-2130<br />
<br />
Ungar B.L. Cryptosporidiosis in humans (homo sapiens), in Cryptosporidiosis in man and animals. (CRC Press, Boca Raton, FL, 1990). 59-82<br />
<br />
=== [[Ingestion during Swimming|Ingestion during Swimming]] ===<br />
<br />
''' [http://wiki.camra.msu.edu/index.php?title=File:Ingestion_during_swimming.xls Table 1. Water Ingestion by selected groups of swimmers]'''<br />
<br />
Original survey data can be obtained by [http://wiki.camra.msu.edu/index.php?title=File:Ingestion_Results_Pilot_Study-2.xls this link]. <br /><br />
<br />
{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
| '''Population''' || '''Age'''|| ''' Number of Subjects '''|| '''Average water ingestion (mL/45 minute)'''||'''Standard deviation'''<br />
|-<br />
| United States Adults<br />
| Older than 18 years old<br />
| 12<br />
| 16 <br />
| 19<br />
|-<br />
| United States Non-adults<br />
| 18 years old and younger<br />
| 41<br />
| 37<br />
| 31<br />
|}<br />
|}<br />
a: Non-parametric Wilcoxon Rank Sum test was applied for statistical analysis. <br /> b: The comparison between adults and non-adults are significantly different at 5% level. The comparison between different gender groups was evaluated and not significant.<br /> Dufour A, Evans O, Behymer T and Cantu R. (2006) Water ingestion during swimming activities in a pool: A pilot study. Journal of Water Health. 04.4: 425-430. [http://www.pwtag.org/researchdocs/Used%20Ref%20docs/14%20Water%20Ingetion%20study%20%28Dufour%29.pdf Full text]<br />
<br />
<br />
<br />
=== Respiration ===<br />
<br />
==== Pathogen Excretion Rate ====<br />
<br />
=== [[Drinking Water|Drinking Water]] ===<br />
<br />
=== Human Fomite Interaction ===<br />
<br />
=== Droplet Spray ===<br />
<br />
<br />
<br />
----<br />
<br />
<big> Besides exposure assessment, the other major components of microbial risk assessment are [[hazard identification]], [[dose response assessment]], and [[risk characterization]] </big></div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Fecal_Output&diff=2081Fecal Output2011-08-19T19:22:05Z<p>Yh: </p>
<hr />
<div>{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fecal_output_for_different_populations.xls '''Table 1: Fecal Output for Different Populations''']<br />
| '''Population''' || '''Age''' || '''Number of Subjects'''|| '''Mean''' || '''Standard Deviation''' || '''References'''<br />
|-<br />
| colspan = "6" | Healthy people<br />
|-<br />
| Healthy Nigerian Children || 6 months to 5 years || 410 || 109.3 mL/day || 54.07 || Akinbami et al. 1995<br />
|-<br />
| Healthy Nigerian Adult || 23-28 years || 37 || 143.3 g/day || 48.5 || Ogunbiyi 1978<br />
|-<br />
| Healthy British Children and Adult || 11-56 years || 17 || 153 g/day || 79 || Davies et al. 1986<br />
|-<br />
| Healthy American Adult || Adult || 115 || 123.6 g/day || 40.2 || Rendtorff et al. 1967<br />
|-<br />
| colspan = "6" | Ill people<br />
|-<br />
| Indian Children with diarrhea<sup>a</sup> || 3 to 24 months || 70 || 126 g/kilogram of body weight/hour <br />during initial 24 hours and rehydration period || 33.6 || Dutta et al. 2000<br />
|-<br />
| Tunisian Adult with acute diarrhea<sup>b</sup> || over 18 years || 70 || 499 g/day || 284 || Hamza et al. 1999<br />
|-<br />
| American Adult infected with Enteropathogenic Escherichia coli || 18-40 years || 11 || 406 g/day || 300 || Donnenberg 1993<br />
|}<br />
|}<br />
<sup>a</sup> In this study, a high isolation rate of different enteropathogens, e.g. ''Shigella flexneri'', ''Shigella boydii'',''Salmonella typhimurium'', Enteroagrregative ''E. coli'', Enteropathogenic ''E. coli'', ''Aeromonus sp.'' and ''rotavirus'' was observed. <br /><br />
<sup>b</sup> ''Shigella'', ''E. coli'', ''Proteus'', ''Pseudomonas aeruginosa'' were identified from the subjects. <br /><br />
<br />
=== References ===<br />
Akinbami, F., Erinoso, O. and Akinwolere, O. (1995) Defaecation pattern and intestinal transit in nigerian children. African Journal of Medicine and Medical Sciences 24, 337-341. [http://www.ncbi.nlm.nih.gov/pubmed/8886147 Abstract] <br /><br />
<br />
Dutta, P., Dutta, S., Manna, B., Chatterjee, M. and De, A. (2000) Hypo-osmolar oral rehydration salts solution in dehydrating persistent diarrhoea in children: Double-blind, randomized, controlled clinical trial. Acta Paediatrica 89, 411-416. [http://onlinelibrary.wiley.com/doi/10.1111/j.1651-2227.2000.tb00078.x/pdf Full text]<br /><br />
<br />
Hamza, H., Ben Khalifa, H., Baumer, P., Berard, H. and Lecomte, J.M. (1999) Racecadotril versus placebo in the treatment of acute diarrhoea in adults. Alimentary Pharmacology and Therapeutics 13, 15-19. [http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2036.1999.00002.x-i1/pdf Full text] <br /><br />
<br />
Ogunbiyi, T.A. (1978) Whole-gut transit rates and wet stool weight in an urban Nigerian population. World Journal of Surgery 2(3), 387-392. [http://www.springerlink.com/content/v6766540524h765l/fulltext.pdf] <br /></div>Yhhttp://qmrawiki.canr.msu.edu/index.php?title=Fecal_Output&diff=2080Fecal Output2011-08-19T19:18:15Z<p>Yh: </p>
<hr />
<div>{|<br />
| STYLE="vertical-align: top; text-align: center"|<br />
{| border = "1"<br />
|+ [http://wiki.camra.msu.edu/index.php?title=File:Fecal_output_for_different_populations.xls '''Table 1: Fecal Output for Different Populations''']<br />
| '''Population''' || '''Age''' || '''Number of Subjects'''|| '''Mean''' || '''Standard Deviation''' || '''References'''<br />
|-<br />
| colspan = "6" | Healthy people<br />
|-<br />
| Healthy Nigerian Children || 6 months to 5 years || 410 || 109.3 mL/day || 54.07 || Akinbami et al. 1995<br />
|-<br />
| Healthy Nigerian Adult || 23-28 years || 37 || 143.3 g/day || 48.5 || Ogunbiyi 1978<br />
|-<br />
| Healthy British Children and Adult || 11-56 years || 17 || 153 g/day || 79 || Davies 1986<br />
|-<br />
| Healthy US Adult || Adult || 115 || 123.6 g/day || 40.2 || Rendtorff 1967<br />
|-<br />
| colspan = "6" | Ill people<br />
|-<br />
| Indian Children with diarrhea<sup>a</sup> || 3 to 24 months || 70 || 126 g/kilogram of body weight/hour <br />during initial 24 hours and rehydration period || 33.6 || Dutta et al. 2000<br />
|-<br />
| Tunisian Adult with acute diarrhea<sup>b</sup> || over 18 years || 70 || 499 g/day || 284 || Hamza et al. 1999<br />
|-<br />
| Tunisian Adult with acute diarrhea<sup>b</sup> || over 18 years || 70 || 499 g/day || 284 || Hamza et al. 1999<br />
|}<br />
|}<br />
<sup>a</sup> In this study, a high isolation rate of different enteropathogens, e.g. ''Shigella flexneri'', ''Shigella boydii'',''Salmonella typhimurium'', Enteroagrregative ''E. coli'', Enteropathogenic ''E. coli'', ''Aeromonus sp.'' and ''rotavirus'' was observed. <br /><br />
<sup>b</sup> ''Shigella'', ''E. coli'', ''Proteus'', ''Pseudomonas aeruginosa'' were identified from the subjects. <br /><br />
<br />
=== References ===<br />
Akinbami, F., Erinoso, O. and Akinwolere, O. (1995) Defaecation pattern and intestinal transit in nigerian children. African Journal of Medicine and Medical Sciences 24, 337-341. [http://www.ncbi.nlm.nih.gov/pubmed/8886147 Abstract] <br /><br />
<br />
Dutta, P., Dutta, S., Manna, B., Chatterjee, M. and De, A. (2000) Hypo-osmolar oral rehydration salts solution in dehydrating persistent diarrhoea in children: Double-blind, randomized, controlled clinical trial. Acta Paediatrica 89, 411-416. [http://onlinelibrary.wiley.com/doi/10.1111/j.1651-2227.2000.tb00078.x/pdf Full text]<br /><br />
<br />
Hamza, H., Ben Khalifa, H., Baumer, P., Berard, H. and Lecomte, J.M. (1999) Racecadotril versus placebo in the treatment of acute diarrhoea in adults. Alimentary Pharmacology and Therapeutics 13, 15-19. [http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2036.1999.00002.x-i1/pdf Full text] <br /><br />
<br />
Ogunbiyi, T.A. (1978) Whole-gut transit rates and wet stool weight in an urban Nigerian population. World Journal of Surgery 2(3), 387-392. [http://www.springerlink.com/content/v6766540524h765l/fulltext.pdf] <br /></div>Yh