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

doi:10.1111/1467-842X.00300

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

9 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 language	English
Pages (from-to)	491-502
Number of pages	12
Journal	Australian and New Zealand Journal of Statistics
Volume	45
Issue number	4
DOIs	https://doi.org/10.1111/1467-842X.00300
Publication status	Published - 2003 Dec
Externally published	Yes

Keywords

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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10.1111/1467-842X.00300

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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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AU - Upton, Darren

AU - Yip, Paul S.F.

PY - 2003/12

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AB - 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.

KW - Bayesian inference

KW - Epidemic

KW - Gibbs sampler

KW - Markov chain Monte Carlo methods

KW - Metropolis-Hastings algorithm

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JF - Australian and New Zealand Journal of Statistics

IS - 4

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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