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)

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.

Original languageEnglish
Pages (from-to)491-502
Number of pages12
JournalAustralian and New Zealand Journal of Statistics
Volume45
Issue number4
Publication statusPublished - 2003 Dec
Externally publishedYes

Fingerprint

Stochastic Epidemic Models
Bayesian inference
Markov Chain Monte Carlo Methods
Model Uncertainty
Prior distribution
Posterior distribution
Model

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Bayesian inference for a stochastic epidemic model with uncertain numbers of susceptibles of several types. / Hayakawa, Yu; O'Neill, Philip D.; Upton, Darren; Yip, Paul S F.

In: Australian and New Zealand Journal of Statistics, Vol. 45, No. 4, 12.2003, p. 491-502.

Research output: Contribution to journalArticle

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