Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology

Abstract

A methodology is proposed to derive Bayesian experimental designs for discriminating between rival epidemiological models with computationally intractable likelihoods. Methods from approximate Bayesian computation are used to facilitate inference in this setting, and an efficient implementation of this inference framework for approximating the expectation of utility functions is proposed. Three utility functions for model discrimination are considered, and the performance each utility is explored in designing experiments for discriminating between three epidemiological models; the death model, the Susceptible–Infected model, and the Susceptible–Exposed–Infected model. The challenge of efficiently locating optimal designs is addressed by an adaptation of the coordinate exchange algorithm which exploits parallel computational architectures.