We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

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

McKinley, T. J. and Vernon, I. and Andrianakis, I. and McCreesh, N. and Oakley, J. E. and Nsubuga, R. and Goldstein, M. and White, R. G. (2018) 'Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models.', Statistical science., 33 (1). pp. 4-18.


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.

Item Type:Article
Full text:(AM) Accepted Manuscript
Download PDF
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution.
Download PDF
Publisher Web site:
Publisher statement:This work is licensed under CC BY 4.0
Date accepted:10 June 2017
Date deposited:20 September 2017
Date of first online publication:02 February 2018
Date first made open access:22 February 2018

Save or Share this output

Look up in GoogleScholar