Bayesian inference for a stochastic epidemic model with uncertain numbers of susceptibles of several types

Yu Hayakawa, Philip D. O'Neill, Darren Upton, Paul S.F. Yip

Research output: Contribution to journalArticle

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)491-502
Number of pages12
JournalAustralian and New Zealand Journal of Statistics
Issue number4
Publication statusPublished - 2003 Dec



  • Bayesian inference
  • Epidemic
  • Gibbs sampler
  • Markov chain Monte Carlo methods
  • Metropolis-Hastings algorithm

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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