Open Access
September 2019 Bayes Factors for Partially Observed Stochastic Epidemic Models
Muteb Alharthi, Theodore Kypraios, Philip D. O’Neill
Bayesian Anal. 14(3): 907-936 (September 2019). DOI: 10.1214/18-BA1134


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.


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Muteb Alharthi. Theodore Kypraios. Philip D. O’Neill. "Bayes Factors for Partially Observed Stochastic Epidemic Models." Bayesian Anal. 14 (3) 907 - 936, September 2019.


Published: September 2019
First available in Project Euclid: 11 June 2019

zbMATH: 1421.62147
MathSciNet: MR3960776
Digital Object Identifier: 10.1214/18-BA1134

Primary: 62P10
Secondary: 62F15

Keywords: Bayes factor , power posterior , Stochastic epidemic model

Vol.14 • No. 3 • September 2019
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