TY - JOUR

T1 - Bayesian inference for partially observed stochastic epidemics.

AU - O'Neil, P. D.

AU - Roberts, G. O.

PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

KW - Bayesian inference • Epidemic • General stochastic epidemic • Gibbs sampler • Hastings algorithm • Markov chain Monte Carlo methods • Reed–Frost epidemic

U2 - 10.1111/1467-985X.00125

DO - 10.1111/1467-985X.00125

M3 - Journal article

VL - 162

SP - 121

EP - 129

JO - Journal of the Royal Statistical Society: Series A Statistics in Society

JF - Journal of the Royal Statistical Society: Series A Statistics in Society

SN - 0964-1998

IS - 1

ER -