Bayesian model discrimination for partially-observed epidemic models

被引:3
作者
Walker, James N. [1 ,2 ]
Black, Andrew J. [1 ,2 ]
Ross, Joshua V. [1 ,2 ]
机构
[1] Univ Adelaide, Sch Math Sci, Adelaide, SA 5005, Australia
[2] Univ Adelaide, Sch Math Sci, ACEMS, Adelaide, SA 5005, Australia
基金
澳大利亚研究理事会; 澳大利亚国家健康与医学研究理事会;
关键词
Model selection; Epidemic modelling; Importance sampling; Markov chain; Particle filter; TIME-SERIES; SELECTION; SMC2; DISTRIBUTIONS; COMPUTATION; INFERENCE; CHOICE; SIZE;
D O I
10.1016/j.mbs.2019.108266
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:13
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