Table of Recommended Best-Fit Parameters
Please click on the tab headings to navigate betweeen tabs.| We generally recommend a single dose response model, and we justify the decision in terms of these 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.
*Please click on the tab headings to navigate between tabs.
Agent | Best fit model* | Optimized parameter(s) | LD_{50}/ID_{50} | Host type | Agent strain | Route | # of doses | Dose units | Response | Reference |
---|---|---|---|---|---|---|---|---|---|---|
Bacillus anthracis: Dose Response Models | exponential | k = 1.65E-05 | 4.2E+04 | guinea pig | Vollum | inhalation | 4 | spores | death | Druett 1953 |
Burkholderia pseudomallei: Dose Response Models | beta-Poisson | α = 3.28E-01 , N_{50} = 5.43E+03 | 5.43E+03 | C57BL/6 mice and diabetic rat | KHW,316c | intranasal,intraperitoneal | 10 | CFU | death | Liu, Koo et al. 2002 and Brett and Woods 1996 |
Campylobacter jejuni and Campylobacter coli: Dose Response Models | beta-Poisson | α= 1.44E-01 , N_{50} = 8.9E+02 | 8.9E+02 | human | strain A3249 | oral (in milk) | 6 | CFU | infection | Black et al 1988 |
Coxiella burnetii: Dose Response Models | beta-Poisson | α= 3.57E-01 , N_{50} = 4.93E+08 | 4.93E+08 | C57BL/1OScN mice | phase I Ohio | intraperitoneal | 10 | PFU | death | Williams et al, 1982 |
Escherichia coli enterohemorrhagic (EHEC): Dose Response Models | exponential | k=2.18E-04 | 3.18E+03 | pig | EHEC O157:H7, strain 86-24 | oral (in food) | 3 | CFU | shedding in feces | Cornick & Helgerson (2004) |
Escherichia coli: Dose Response Models | beta-Poisson | α = 1.55E-01 , N_{50} = 2.11E+06 | 2.11E+06 | human | EIEC 1624 | oral (in milk) | 3 | CFU | positive stool isolation | DuPont et al. (1971) |
Francisella tularensis: Dose Response Models | exponential | k = 4.73E-02 | 1.46E+01 | monkeys | SCHU S-4 | inhalation | 4 | CFU | death | Day and Berendt, 1972 |
Legionella pneumophila: Dose Response Models | exponential | k = 5.99E-02 | 1.16E+01 | guinea pig | Philadelphia 1 | inhalation | 4 | CFU | infection | Muller et al. (1983) |
Listeria monocytogenes (Death as response): Dose Response Models | exponential | k = 1.15E-05 | 6.05E+04 | C57B1/6J mice | F5817 | oral | 6 | CFU | death | Golnazarian, Donnelly et al. 1989 |
Listeria monocytogenes (Infection): Dose Response Models | beta-Poisson | α = 2.53E-01 , N_{50} = 2.77E+02 | 2.77E+02 | C57Bl/6J mice | F5817 | oral | 10 | CFU | infection | Golnazarian |
Listeria monocytogenes (Stillbirths): Dose Response Models | beta-Poisson | α = 4.22E-02 , N_{50} = 1.78E+09 | 1.78E+09 | pooled | oral | 13 | CFU | stillbirths | Smith, Williams2007 | |
Mycobacterium avium: Dose Response Models | exponential | k = 6.93E-04 | 1E+03 | deer | sub sp. Paratuberculosis Bovine | oral | 3 | CFU | infection | O'Brien et al(1976) |
Pseudomonas aeruginosa (Contact lens): Dose Response Models | beta-Poisson | α = 1.9E-01 , N_{50} = 1.85E+04 | 1.85E+04 | white rabbit | contact lens | 10 | CFU | corneal ulceration | Lawin-Brussel et al. (1993) | |
Pseudomonas aeruginosa (bacterimia): Dose Response Models | exponential | k = 1.05E-04 | 6.61E+03 | Swiss webster mice (5day old) | ATCC 19660 | injected in eyelids | 12 | CFU | death | Hazlett, Rosen et al. 1978 |
Rickettsia rickettsi: Dose Response Models | beta-Poisson | α= 7.77E-01 , N_{50} = 2.13E+01 | 2.13E+01 | Pooled data | R1 and Sheila Smith | NA | 27 | CFU | morbidity | Saslaw and Carlisle 1966 and Dupont, Hornick et al. 1973 |
Salmonella Typhi: Dose Response Models | beta-Poisson | α = 1.75E-01 , N_{50} = 1.11E+06 | 1.11E+06 | human | Quailes | oral, in milk | 8 | CFU | disease | Hornick et al. (1966),Hornick et al. (1970) |
Salmonella anatum: Dose Response Models | beta-Poisson | α= 3.18E-01 , N_{50} = 3.71E+04 | 3.71E+04 | human | strain I | oral, with eggnog | 16 | CFU | positive stool culture | McCullough and Elsele,1951 |
Salmonella meleagridis: Dose Response Models | beta-Poisson | α= 3.89E-01 , N_{50} = 1.68E+04 | 1.68E+04 | human | strain I | oral, with eggnog | 11 | CFU | infection | McCullough and Eisele 1951,2 |
Salmonella nontyphoid: Dose Response Models | beta-Poisson | α= 2.1E-01 , N_{50} = 4.98E+01 | 4.98E+01 | mice | strain 216 and 219 | intraperitoneal | 10 | CFU | death | Meynell and Meynell,1958 |
Salmonella serotype newport: Dose Response Models | exponential | k = 3.97E-06 | 1.74E+05 | human | Salmonella newport | oral | 3 | CFU | infection | McCullough and Elsele,1951 |
Shigella: Dose Response Models | beta-Poisson | α= 2.65E-01 , N_{50} = 1.48E+03 | 1.48E+03 | human | 2a (strain 2457T) | oral (in milk) | 4 | CFU | positive stool isolation | DuPont et al. (1972b) |
Staphylococcus aureus: Dose Response Models | exponential | k = 7.64E-08 | 9.08E+06 | human | subcutaneous | 6 | CFU/cm^{2} | infection | Rose and Haas 1999 | |
TestPage | exponential | k = 1.65E-05 | 4.2E+04 | guinea pig | Vollum | inhalation | 4 | spores | death | Druett 1953 |
Vibrio cholerae: Dose Response Models | beta-Poisson | α= 2.50E-01 , N_{50} = 2.43E+02 | 2.43E+02 | human | Inaba 569B | oral (with NaHCO3) | 6 | CFU | infection | Hornick et al., (1971) |
Yersinia pestis: Dose Response Models | exponential | k = 1.63E-03 | 4.26E+02 | mice | CO92 | intranasal | 4 | CFU | death | Lathem et al. 2005 |
*These models are preferred in most circumstances. However, consider all available models to decide which one is most appropriate for your analysis.
Agent | Best fit model* | Optimized parameter(s) | LD_{50}/ID_{50} | Host type | Agent strain | Route | # of doses | Dose units | Response | Reference |
---|---|---|---|---|---|---|---|---|---|---|
Adenovirus: Dose Response Models | exponential | k = 6.07E-01 | 1.14E+00 | human | type 4 | inhalation | 4 | TCID_{50} | infection | Couch, Cate et al. 1966 |
Echovirus: Dose Response Models | beta-Poisson | α = 1.06E+00 , N_{50} = 9.22E+02 | 9.22E+02 | human | strain 12 | oral | 4 | PFU | infection | Schiff et al.,1984 |
Enteroviruses: Dose Response Models | exponential | k = 3.74E-03 | 1.85E+02 | pig | porcine, PE7-05i | oral | 3 | PFU | infection | Cliver, 1981 |
Influenza: Dose Response Models | beta-Poisson | α = 5.81E-01 , N_{50} =9.45E+05 | 9.45E+05 | human | H1N1,A/California/10/78 attenuated strain, H3N2,A/Washington/897/80 attenuated strain |
intranasal | 9 | TCID_{50} | infection | Murphy et al., 1984 & Murphy et al., 1985 |
Lassa virus: Dose Response Models | exponential | k = 2.95E+00 | 2.35E-01 | guinea pig | Josiah strain | subcutaneous | 6 | PFU | death | Jahrling et al., 1982 |
Poliovirus: Dose Response Models | exponential | k = 4.91E-01 | 1.41E+00 | human | type 1,attenuated | oral (capsule) | 3 | PD50 (mouse paralytic doses) | alimentary infection | Koprowski |
Rhinovirus: Dose Response Models | beta-Poisson | α = 2.21E-01 , N_{50} = 1.81E+00 | 1.81E+00 | human | type 39 | intranasal | 6 | TCID50 doses | infection | Hendley et al., 1972 |
Rotavirus: Dose Response Models | beta-Poisson | α = 2.53E-01 , N_{50} = 6.17E+00 | 6.17E+00 | human | CJN strain (unpassaged) | oral | 8 | FFU | infection | Ward et al, 1986 |
SARS: Dose Response Models | exponential | k = 2.46E-03 | 2.82E+02 | mice hACE-2 and A/J | rSARS-CoV | intranasal | 8 | PFU | death | DeDiego et al., 2008 & De Albuquerque et al., 2006 |
*These models are preferred in most circumstances. However, consider all available models to decide which one is most appropriate for your analysis.
Agent | Best fit model* | Optimized parameter(s) | LD_{50}/ID_{50} | Host type | Agent strain | Route | # of doses | Dose units | Response | Reference |
---|---|---|---|---|---|---|---|---|---|---|
Cryptosporidium parvum and Cryptosporidium hominis: Dose Response Models | exponential | k = 5.72E-02 | 1.21E+01 | human | TAMU isolate | oral | 4 | oocysts | infection | Messner et al. 2001 |
Endamoeba coli: Dose Response Models | beta-Poisson | α = 1.01E-01 , N_{50} = 3.41E+02 | 3.41E+02 | human | From an infected human | oral | 5 | Cysts | infection | Rendtorff 1954 |
Giardia duodenalis: Dose Response Models | exponential | k = 1.99E-02 | 3.48E+01 | human | From an infected human | oral | 8 | Cysts | infection | Rendtorff 1954 |
Naegleria fowleri: Dose Response Models | exponential | k = 3.42E-07 | 2.03E+06 | mice | LEE strain | intravenous | 7 | no of trophozoites | death | Adams et al. 1976 & Haggerty and John 1978 |
*These models are preferred in most circumstances. However, consider all available models to decide which one is most appropriate for your analysis.
Agent | Best fit model* | Optimized parameter(s) | LD_{50}/ID_{50} | Host type | Agent strain | Route | # of doses | Dose units | Response | Reference |
---|---|---|---|---|---|---|---|---|---|---|
PrP prions: Dose Response Models | beta-Poisson | α = 1.76E+00 , N_{50} = 1.04E+05 | 1.04E+05 | hamsters | scrapie strain 263k | oral | 5 | LD_{50} i.c. | death | Diringer et al. 1998 |
*These models are preferred in most circumstances. However, consider all available models to decide which one is most appropriate for your analysis.
We prefer dose response models with the following criteria, in rough order of importance:
- Statistically acceptable fit (fail to reject goodness of fit, p > 0.05)
- Human subjects, or animal models that mimic human pathophysiology well
- Infection as the response, rather than disease, symptoms, or death
- Exposure route similar/identical to the exposure route of natural infection
- Pathogen strain is similar to strains causing natural infection
- 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)
- Low ID_{50}/LD_{50} (to obtain a conservative risk estimate)
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.