Bayesian model choice for epidemic models with two levels of mixing

Knock, Edward S.; O'Neill, Philip D.

Abstract

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. © 2013 The Author 2013. Published by Oxford University Press.

Citation

O'Neill, P., & Knock, E. (2014). Bayesian model choice for epidemic models with two levels of mixing. Biostatistics, 15(1), 46-59. https://doi.org/10.1093/biostatistics/kxt023

• Journal Article Type: Article
• Acceptance Date: Jun 29, 2013
• Online Publication Date: Jul 24, 2013
• Publication Date: Jan 1, 2014
• Deposit Date: Feb 15, 2018
• Journal: Biostatistics
• Print ISSN: 1465-4644
• Electronic ISSN: 1468-4357
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 1
• Pages: 46-59
• DOI: https://doi.org/10.1093/biostatistics/kxt023
• Public URL: https://nottingham-repository.worktribe.com/output/1095609
• Publisher URL: https://academic.oup.com/biostatistics/article/15/1/46/244669
• PMID: 23887980