Bayesian inference for partially observed stochastic epidemics.

O'Neil, P. D. and Roberts, G. O. (1999) Bayesian inference for partially observed stochastic epidemics. Journal of the Royal Statistical Society: Series A Statistics in Society, 162 (1). pp. 121-129. ISSN 0964-1998

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series A Statistics in Society
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
ID Code:
19426
Deposited By:
Deposited On:
18 Nov 2008 15:01
Refereed?:
Yes
Published?:
Published
Last Modified:
27 May 2020 01:48