Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/122760
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Type: Journal article
Title: Bayesian model discrimination for partially-observed epidemic models
Author: Walker, J.N.
Black, A.J.
Ross, J.V.
Citation: Mathematical Biosciences, 2019; 317:108266-1-108266-13
Publisher: Elsevier
Issue Date: 2019
ISSN: 0025-5564
1879-3134
Statement of
Responsibility: 
James N. Walker, Andrew J. Black, Joshua V. Ross
Abstract: An efficient method for Bayesian model selection is presented for a broad class of continuous-time Markov chain models and is subsequently applied to two important problems in epidemiology. The first problem is to identify the shape of the infectious period distribution; the second problem is to determine whether individuals display symptoms before, at the same time, or after they become infectious. In both cases we show that the correct model can be identified, in the majority of cases, from symptom onset data generated from multiple outbreaks in small populations. The method works by evaluating the likelihood using a particle filter that incorporates a novel importance sampling algorithm designed for partially-observed continuous-time Markov chains. This is combined with another importance sampling method to unbiasedly estimate the model evidence. These come with estimates of precision, which allow for stopping criterion to be employed. Our method is general and can be applied to a wide range of model selection problems in biological and epidemiological systems with intractable likelihood functions.
Keywords: Model selection; epidemic modelling; importance sampling; Markov chain; particle filter
Rights: © 2019 Elsevier Inc. All rights reserved.
RMID: 1000001354
DOI: 10.1016/j.mbs.2019.108266
Grant ID: http://purl.org/au-research/grants/arc/FT130100254
http://purl.org/au-research/grants/arc/DE160100690
Appears in Collections:Mathematical Sciences publications

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