Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study

Biometrics

Volumn 62, Issue 4, 2006, Pages 1170-1177

  Lekone, Phenyo E ^a   Finkenstädt, Bärbel F ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Control intervention; Ebola epidemics; Estimating transition rates; Latent process; Stochastic SEIR model

Indexed keywords

DISEASE CONTROL; DISEASES; MARKOV PROCESSES; MONTE CARLO METHODS; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

CONTROL INTERVENTION; EBOLA EPIDEMIC; ESTIMATING TRANSITION RATE; LATENT PROCESS; STOCHASTIC SUSCEPTIBLE-EXPOSED-INFECTIOUS-RECOVERED MODEL; STOCHASTIC-MODELING; STOCHASTICS; SUSCEPTIBLE-EXPOSED-INFECTIOUS-RECOVERED; TIMES SERIES; TRANSITION RATES;

TIME SERIES;

EPIDEMIC; INFECTIOUS DISEASE; MARKOV CHAIN; MONTE CARLO ANALYSIS; MORTALITY; NUMERICAL MODEL; STOCHASTICITY; VIRUS;

ARTICLE; EPIDEMIC; HUMAN; INCIDENCE; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; STOCHASTIC MODEL; VIRUS HEMORRHAGIC FEVER; VIRUS TRANSMISSION;

AFRICA; CENTRAL AFRICA; CONGO; SUB-SAHARAN AFRICA;

EID: 33845491978     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2006.00609.x     Document Type: Article

References (26)

1. Anderson, R. M. and May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. New York: Oxford University Press.
2. Andersson, H. (1999). Epidemic models and social networks. Mathematical Scientist 24, 128-147.
3. Andersson, H. and Britton, T. (2000). Stochastic Epidemic Models and Their Statistical Analysis. New York: Springer.
4. Bailey, N. T. J. (1975). The Mathematical Theory of Infectious Diseases and Its Applications. London: Griffin.
5. Ball, F. G., Mollison, D., and Scalia-Tomba, G. (1997). Epidemics with two levels of mixing. Annals of Applied Probability 7, 46-89.
6. Becker, N. G. (1976). On a general stochastic epidemic model. Theoretical Population Biology 11, 23-26.
7. Breman, J. G., Piot, P., Johnson, K. M., et al. (1977). The epidemiology of Ebola hemorrhagic fever in Zaire, 1976. In Proceedings of the International Colloquium on Ebola Virus Infections, 103-124. Antwerp, Belgium: Elsevier/North Holland Biomedical Press.
8. Chib, S. and Greenberg, E. (1994). Bayes inference in regression models with ARMA(p,q) errors. Journal of Econometrics 64, 183-206.
9. Chowell, G., Hengartner, N. W., Castillo-Chavez, C., Fenimore, P. W., and Hyman, J. M. (2004). The basic reproductive number of Ebola and the effects of public health measures: The cases of Congo and Uganda. Journal of Theoretical Biology 229, 119-126.
10. Diekmann, O., Heesterbeek, J., and Metz, J. (1990). On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology 28, 365-382.
11. Doornik, J. A. (1999). Object-Orientated Matrix Programming Using Ox, Version 2.1. London: Timberlake Consultants.
12. Durham, G. and Gallant, A. R. (2002). Numerical techniques for maximum likelihood estimation of continuous-time diffusion processes (with discussion). Journal of Business and Economic Statistics 20, 297-338.
13. Elerian, O., Chib, S., and Shephard, N. (2001). Likelihood inference for discretely observed nonlinear diffusions. Econometrica 4, 959-993.
14. Fearnhead, P. and Meligkotsidou, L. (2004). Exact filtering for partially-observed continuous time models. Journal of the Royal Statistical Society, Series B 66, 771-789.
15. Gelman, A., Meng, X. L., and Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica 6, 733-807.
16. Gibson, G. J. and Renshaw, E. (1998). Estimating parameters in stochastic compartmental models using Markov chain methods. IMA Journal of Mathematics in Applied Medicine and Biology 15, 19-40.
17. Hethcote, H. W. (2000). The mathematics of infectious diseases. Society for Industrial and Applied Mathematics 42, 599-653.
18. Khan, A. S., Tshioko, F. K., Heymann, D. L., et al. (1999). The reemergence of Ebola hemorrhagic fever, Democratic Republic of the Congo, 1995. Journal of Infectious Diseases 179, 76-86.
19. Mode, C. J. and Sleeman, C. K. (2000). Stochastic Processes in Epidemiology. Singapore: World Scientific.
20. Neal, P. J. and Roberts, G. O. (2004). Statistical inference and model selection for the 1861 Hagelloch measles epidemic. Biostatistics 5, 249-261.
21. O'Neill, P. D. (2002). A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo Methods. Mathematical Biosciences 180, 103-114.
22. O'Neill, P. D. and Roberts, G. O. (1999). Bayesian inference for partially observed stochastic epidemics. Journal of the Royal Statistical Society, Series B 162, 121-129.
23. Riley, S., Fraser, C., Donnelly, C. A., et al. (2003). Transmission dynamics of the etiological agent of SARS in Hong Kong: Impact of public health interventions. Science 300, 1961-1966.
24. Roberts, G. O. and Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science 16, 351-367.
25. Streftaris, G. and Gibson, G. J. (2004). Bayesian inference for stochastic epidemics in closed populations. Statistical Modelling 4, 63-75.
26. World Health Organization. (2003). Ebola haemorrhagic fever: Disease outbreaks. Available at: http:/www. who.int/disease-outbreaks-news/disease/A98. 4htm (accessed on February 5, 2005).