Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -