Bayesian Analysis

Bayes Factors for Partially Observed Stochastic Epidemic Models

Muteb Alharthi, Theodore Kypraios, and Philip D. O’Neill

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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.

Article information

Bayesian Anal., Volume 14, Number 3 (2019), 907-936.

First available in Project Euclid: 11 June 2019

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62P10: Applications to biology and medical sciences
Secondary: 62F15: Bayesian inference

Bayes factor power posterior stochastic epidemic model

Creative Commons Attribution 4.0 International License.


Alharthi, Muteb; Kypraios, Theodore; O’Neill, Philip D. Bayes Factors for Partially Observed Stochastic Epidemic Models. Bayesian Anal. 14 (2019), no. 3, 907--936. doi:10.1214/18-BA1134.

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