Pair-based likelihood approximations for stochastic epidemic models

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

Abstract

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.

Citation

Stockdale, J. E., Kypraios, T., & O'Neill, P. D. (2021). Pair-based likelihood approximations for stochastic epidemic models. Biostatistics, 22(3), 575-597. https://doi.org/10.1093/biostatistics/kxz053

• 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
• DOI: https://doi.org/10.1093/biostatistics/kxz053
• Keywords: Epidemic models; Likelihood approximation; Markov chain Monte Carlo methods; Stochastic epidemic models
• Public URL: https://nottingham-repository.worktribe.com/output/3283042
• Publisher URL: https://academic.oup.com/biostatistics/article/22/3/575/5663563