Bayesian inference for partially observed stochastic epidemics

P. D. O’Neill and G. O. Roberts

Journal of the Royal Statistical Society Series A, 1999, vol. 162, issue 1, 121-129

Abstract: The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data concerning the process of infection are seldom available. Consequently, standard statistical techniques can become too complicated to implement effectively. In this paper Markov chain Monte Carlo methods are used to make inferences about the missing data as well as the unknown parameters of interest in a Bayesian framework. The methods are applied to real life data from disease outbreaks.

Date: 1999
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