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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.

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Authors

T.J. McKinley

I. Andrianakis

N. McCreesh

J.E. Oakley

R. Nsubuga

M. Goldstein

R.G. White



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 Mar 29, 2024
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

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