Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models

McKinley, T.J.; Vernon, I.; Andrianakis, I.; McCreesh, N.; Oakley, J.E.; Nsubuga, R.; Goldstein, M.; White, R.G.

Abstract

Approximate Bayesian Computation (ABC) and other simulation-based inference methods are becoming increasingly used for inference in complex systems, due to their relative ease-of-implementation. We briefly review some of the more popular variants of ABC and their application in epidemiology, before using a real-world model of HIV transmission to illustrate some of challenges when applying ABC methods to high-dimensional, computationally intensive models. We then discuss an alternative approach—history matching—that aims to address some of these issues, and conclude with a comparison between these different methodologies.

Citation

McKinley, T., Vernon, I., Andrianakis, I., McCreesh, N., Oakley, J., Nsubuga, R., …White, R. (2018). Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models. Statistical Science, 33(1), 4-18. https://doi.org/10.1214/17-sts618

• Journal Article Type: Article
• Acceptance Date: Jun 10, 2017
• Online Publication Date: Feb 2, 2018
• Publication Date: Feb 2, 2018
• Deposit Date: Sep 26, 2016
• Publicly Available Date: Sep 20, 2017
• Journal: Statistical Science
• Print ISSN: 0883-4237
• Electronic ISSN: 2168-8745
• Publisher: Institute of Mathematical Statistics
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Issue: 1
• Pages: 4-18
• DOI: https://doi.org/10.1214/17-sts618
• Public URL: https://durham-repository.worktribe.com/output/1404397