Bayes Factors for Partially Observed Stochastic Epidemic Models

Alharthi, Muteb; Kypraios, Theodore; O'Neill, Philip D.

Abstract

We consider the problem of model choice for stochastic epidemic models given partial observation of a disease outbreak through time. Our main focus is on the use of Bayes factors. Although Bayes factors have appeared in the epidemic modelling literature before, they can be hard to compute and little attention has been given to fundamental questions concerning their utility. In this paper we derive analytic expressions for Bayes factors given complete observation through time, which suggest practical guidelines for model choice problems. We adapt the power posterior method for computing Bayes factors so as to account for missing data and apply this approach to partially observed epidemics. For comparison, we
also explore the use of a deviance information criterion for missing data scenarios. The methods are illustrated via examples involving both simulated and real data.

Citation

Alharthi, M., Kypraios, T., & O'Neill, P. D. (2019). Bayes Factors for Partially Observed Stochastic Epidemic Models. Bayesian Analysis, 14(3), 927-956. https://doi.org/10.1214/18-BA1134

• Journal Article Type: Article
• Acceptance Date: Oct 23, 2018
• Publication Date: 2019-09
• Deposit Date: Nov 1, 2018
• Publicly Available Date: Jan 11, 2019
• Journal: Bayesian Analysis
• Print ISSN: 1936-0975
• Electronic ISSN: 1931-6690
• Publisher: International Society for Bayesian Analysis
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 3
• Pages: 927-956
• DOI: https://doi.org/10.1214/18-BA1134
• Keywords: Bayes factor; power posterior; stochastic epidemic model
• Public URL: https://nottingham-repository.worktribe.com/output/1216347
• Publisher URL: https://projecteuclid.org/euclid.ba/1543978839
• Contract Date: Nov 1, 2018