Bayesian Inference for a Stochastic Epidemic Model with Uncertain Numbers of Susceptibles of Several Types |
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| Authors: | Yu Hayakawa Philip D. O'Neill Darren Upton Paul S.F. Yip |
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| Affiliation: | Victoria University of Wellington, New Zealand.; University of Nottingham, University Park, UK.;University of Cambridge, UK;The University of Hong Kong; |
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| Abstract: | A stochastic epidemic model with several kinds of susceptible is used to analyse temporal disease outbreak data from a Bayesian perspective. Prior distributions are used to model uncertainty in the actual numbers of susceptibles initially present. The posterior distribution of the parameters of the model is explored via Markov chain Monte Carlo methods. The methods are illustrated using two datasets, and the results are compared where possible to results obtained by previous analyses. |
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| Keywords: | Bayesian inference epidemic Gibbs sampler Markov chain Monte Carlo methods Metropolis–Hastings algorithm |
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