Approximate Bayesian computation and simulation-based inference for complex stochastic epidemic models. (English) Zbl 1407.62406
Summary: 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.
MSC:
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62F15 | Bayesian inference |
| 92C60 | Medical epidemiology |
Keywords:
approximate Bayesian computation; history matching; emulation; Bayesian inference; infectious disease modelsReferences:
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