Bayesian model choice for epidemic models with two levels of mixing
Bayesian model choice for epidemic models with two levels of mixing
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DOI：
10.1093/biostatistics/kxt023
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发表时间：
2014-01-01
期刊：
影响因子：
2.1
通讯作者：
O'Neill, Philip D.
中科院分区：
文献类型：
--
作者：
Knock, Edward S.;O'Neill, Philip D.
本文考虑了在被划分为家庭的人口中传染病最终结果数据的竞争模型之间的选择问题。流行病模型是随机的基于个体的传染病模型，是一种无传染性的消除型传染病模型。主要的重点是各种算法的估计贝叶斯因子，其中基于路径采样的算法被认为是最好的结果。我们还探讨了模型内先验分布变得越来越缺乏信息的情况下的理论属性，这表明在使用贝叶斯因子作为模型选择工具时需要谨慎。一个合适的形式的偏差信息标准也被认为是比较。的理论和方法都说明了人工数据，流感数据从特库姆塞研究疾病。
This paper considers the problem of choosing between competing models for infectious disease final outcome data in a population that is partitioned into households. The epidemic models are stochastic individual-based transmission models of the susceptible-infective-removed type. The main focus is on various algorithms for the estimation of Bayes factors, of which a path sampling-based algorithm is seen to give the best results. We also explore theoretical properties in the case where the within-model prior distributions become increasingly uninformative, which show the need for caution when using Bayes factors as a model choice tool. A suitable form of deviance information criterion is also considered for comparison. The theory and methods are illustrated with both artificial data, and influenza data from the Tecumseh study of illness.