Salmonella nontyphoid: Dose Response Models

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Salmonella nontyphoid

Author: Yin Huang


General overview of Salmonella and Salmonellosis

Salmonella, a genus of rod-shaped, gram-negative, non-spore forming, predominantly motile enterobacteria, causes more than 104 cases of infections per year in United States. (Chalker and Blaser 1988). Non-typhoidal salmonellae are important causes of many foodborne infections. Approximately 45,000 cases and 600 deaths have been reported annually to the Centers for Disease Control in the past decade. Salmonellae have a wide range of hosts and are strongly associated with agricultural products (Acheson and Hohmann 2001).


http://wwwnc.cdc.gov/eid/article/10/1/pdfs/03-0171.pdf



Summary Data

Meynell G.G. and Meynell E.W. (1958) inoculated albino (PGMS) mice intraperitoneally with the Salmonella typhimurium strains 216 and 219 and challenged albino (Tuck) mice with Salmonella typhimurium strain 533 via the intraperitoneal route. [1]


Experiment serial number Reference Host type Agent strain Route # of doses Dose units Response Best fit model Optimized parameter(s) LD50/ID50
246* [1] mice strain 216 and 219 intraperitoneal 10 CFU death beta-Poisson α= 2.1E-01 , N50 = 4.98E+01 4.98E+01
247 [1] mice strain 533 intraperitoneal 11 CFU death beta-Poisson α = 6.21E-02 , N50 = 3.46E+07 3.46E+07
248 [1] mice strain 533 intraperitoneal 7 CFU death beta-Poisson α= 1.08E-01 , N50 = 9.66E+06 9.66E+06
*This model is preferred in most circumstances. However, consider all available models to decide which one is most appropriate for your analysis.


*Recommended Model

Experiment number 246 is recommended model based on the least LD50 value. Although the strains were different and so the virulences, the estimated risk should be in conservative side.


Exponential and betapoisson model.jpg

Optimization Output for experiment 246 (Salmonella Typhimurium)

Mice/Salmonella strain 216 and 219 data [1]
Dose Dead Survived Total
5 7 8 15
25 4 11 15
125 7 8 15
630 9 6 15
3160 8 7 15
16000 13 2 15
8E+04 15 0 15
4E+05 15 0 15
2E+06 15 0 15
1E+07 15 0 15


Goodness of fit and model selection
Model Deviance Δ Degrees
of freedom
χ20.95,1
p-value
χ20.95,m-k
p-value
Exponential 133 113 9 3.84
0
16.9
0
Beta Poisson 20.5 8 15.5
0.00846
Neither the exponential nor beta-Poisson fits well; beta-Poisson is less bad.


Optimized parameters for the beta-Poisson model, from 10000 bootstrap iterations
Parameter MLE estimate Percentiles
0.5% 2.5% 5% 95% 97.5% 99.5%
α 2.1E-01 1.45E-01 1.58E-01 1.65E-01 2.92E-01 3.14E-01 3.63E-01
N50 4.98E+01 8.10E+00 1.40E+01 1.72E+01 1.31E+02 1.62E+02 2.34E+02


Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.
beta Poisson model plot, with confidence bounds around optimized model


Optimization Output for experiment 247 (Salmonella Typhimurium)

Mice/ Salmonella strain 533 data [1]
Dose Dead Survived Total
603 6 36 42
1910 3 39 42
6030 7 35 42
19100 5 42 47
60300 6 34 40
191000 3 29 32
603000 6 20 26
1910000 7 7 14
6030000 7 5 12
1.91E+07 10 2 12
6.03E+07 13 0 13


Goodness of fit and model selection
Model Deviance Δ Degrees
of freedom
χ20.95,1
p-value
χ20.95,m-k
p-value
Exponential 193 146 10 3.84
0
18.3
0
Beta Poisson 47.5 9 16.9
3.22e-07
Neither the exponential nor beta-Poisson fits well; beta-Poisson is less bad.


Optimized parameters for the beta-Poisson model, from 10000 bootstrap iterations
Parameter MLE estimate Percentiles
0.5% 2.5% 5% 95% 97.5% 99.5%
α 6.21E-02 3.53E-02 4.06E-02 4.32E-02 1.09E-01 1.25E-01 1.80E-01
N50 3.46E+07 9.76E+05 1.69E+06 2.40E+06 9.41E+08 2.13E+09 1.34E+10


Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.
beta Poisson model plot, with confidence bounds around optimized model


Optimization Output for experiment 248 (Salmonella Typhimurium)

Mice/ Salmonella strain 533 data [1]
Dose Dead Survived Total
1E+04 20 180 200
1E+05 17 153 170
1E+06 11 29 40
3160000 6 24 30
1E+07 12 8 20
3.16E+07 17 3 20
1E+08 19 1 20


Goodness of fit and model selection
Model Deviance Δ Degrees
of freedom
χ20.95,1
p-value
χ20.95,m-k
p-value
Exponential 214 165 6 3.84
0
12.6
0
Beta Poisson 48.7 5 11.1
2.56e-09
Neither the exponential nor beta-Poisson fits well; beta-Poisson is less bad.


Optimized parameters for the beta-Poisson model, from 10000 bootstrap iterations
Parameter MLE estimate Percentiles
0.5% 2.5% 5% 95% 97.5% 99.5%
α 1.08E-01 6.19E-02 7.06E-02 7.52E-02 1.79E-01 1.98E-01 2.44E-01
N50 9.66E+06 1.93E+06 2.43E+06 2.82E+06 5.38E+07 8.11E+07 2.13E+08


Parameter scatter plot for beta Poisson model ellipses signify the 0.9, 0.95 and 0.99 confidence of the parameters.
beta Poisson model plot, with confidence bounds around optimized model





References

  1. 1.0 1.1 1.2 1.3 1.4 1.5 1.6 Meynell GG, Meynell EW (1958) The Growth of Micro-Organisms in vivo with Particular Reference to the Relation between Dose and Latent Period Journal of Hygiene 56(3): 323-346.

Acheson D and Hohmann EL (2001) Nontyphoidal Salmonellosis. Clinical Infectious Diseases 32(2), 263-269.

Chalker RB and Blaser MJ (1988) A Review of Human Salmonellosis: III. Magnitude of Salmonella Infection in the United States. Reviews of Infectious Diseases 10(1), 111-124.

Meynell GG and Meynell EW (1958) The growth of micro-organisms in vivo with particular reference to the relation between dose and latent period. Epidemiology & Infection 56(03), 323-346.