Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models
Institute of Mathematical Statistics (IMS)
Reason for embargo
This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Institute of Mathematical Statistics
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.
This work was supported by a Medical Research Council (UK) grant on Model Calibration (MR/J005088/1) (http://www.mrc.ac.uk/).