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Pair-based likelihood approximations for stochastic epidemic models

Stockdale, Jessica E.; Kypraios, Theodore; O'Neill, Philip D.

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Jessica E. Stockdale

Professor of Applied Probability


Fitting stochastic epidemic models to data is a non-standard problem because data on the infection processes defined in such models are rarely observed directly. This in turn means that the likelihood of the observed data is intractable in the sense that it is very computationally expensive to obtain. Although data-augmentated Markov chain Monte Carlo (MCMC) methods provide a solution to this problem, employing a tractable augmented likelihood, such methods typically deteriorate in large populations due to poor mixing and increased computation time. Here we describe a new approach that seeks to approximate the likelihood by exploiting the underlying structure of the epidemic model. Simulation study results show that this approach can be a serious competitor to data-augmented MCMC methods. Our approach can be applied to a wide variety of disease transmission models, and we provide examples with applications to the common cold, Ebola and foot-and-mouth disease.


Stockdale, J. E., Kypraios, T., & O'Neill, P. D. (2021). Pair-based likelihood approximations for stochastic epidemic models. Biostatistics, 22(3), 575-597.

Journal Article Type Article
Acceptance Date Nov 10, 2019
Online Publication Date Dec 6, 2019
Publication Date 2021-07
Deposit Date Nov 14, 2019
Publicly Available Date Dec 7, 2020
Journal Biostatistics
Print ISSN 1465-4644
Electronic ISSN 1468-4357
Publisher Oxford University Press (OUP)
Peer Reviewed Peer Reviewed
Volume 22
Issue 3
Pages 575-597
Keywords Epidemic models; Likelihood approximation; Markov chain Monte Carlo methods; Stochastic epidemic models
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