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Exact Bayesian Inference and Model Selection for Stochastic Models of Epidemics Among a Community of Households

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  • DAMIAN CLANCY
  • PHILIP D. O'NEILL

Abstract

. Much recent methodological progress in the analysis of infectious disease data has been due to Markov chain Monte Carlo (MCMC) methodology. In this paper, it is illustrated that rejection sampling can also be applied to a family of inference problems in the context of epidemic models, avoiding the issues of convergence associated with MCMC methods. Specifically, we consider models for epidemic data arising from a population divided into households. The models allow individuals to be potentially infected both from outside and from within the household. We develop methodology for selection between competing models via the computation of Bayes factors. We also demonstrate how an initial sample can be used to adjust the algorithm and improve efficiency. The data are assumed to consist of the final numbers ultimately infected within a sample of households in some community. The methods are applied to data taken from outbreaks of influenza.

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  • Damian Clancy & Philip D. O'Neill, 2007. "Exact Bayesian Inference and Model Selection for Stochastic Models of Epidemics Among a Community of Households," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(2), pages 259-274, June.
    • Handle: RePEc:bla:scjsta:v:34:y:2007:i:2:p:259-274
    DOI: 10.1111/j.1467-9469.2006.00522.x
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    Cited by:

    1. Lefebvre, Geneviève & Steele, Russell & Vandal, Alain C., 2010. "A path sampling identity for computing the Kullback-Leibler and J divergences," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1719-1731, July.

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