Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods
AbstractA stochastic discrete time version of the susceptible-infected-recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities. Copyright 2005 Royal Statistical Society.
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Bibliographic InfoArticle provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society Series C.
Volume (Year): 54 (2005)
Issue (Month): 3 ()
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- Frits Bijleveld & Jacques Commandeur & Phillip Gould & Siem Jan Koopman, 2005. "Model-based Measurement of Latent Risk in Time Series with Applications," Tinbergen Institute Discussion Papers 05-118/4, Tinbergen Institute.
- Frits Bijleveld & Jacques Commandeur & Phillip Gould & Siem Jan Koopman, 2005.
"Model-based Measurement of Latent Risk in Time Series with Applications,"
Tinbergen Institute Discussion Papers
05-118/4, Tinbergen Institute.
- Frits Bijleveld & Jacques Commandeur & Phillip Gould & Siem Jan Koopman, 2008. "Model-based measurement of latent risk in time series with applications," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 265-277.
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