On the efficient determination of optimal Bayesian experimental designs using ABC: A case study in optimal observation of epidemics

Abstract

We present a new method for determining optimal Bayesian experimental designs, which we refer to as ABCdE. ABCdE uses Approximate Bayesian Computation to calculate the utility of possible designs. For problems with a low-dimensional design space, it evaluates the designs' utility in less computation time compared to existing methods. We apply ABCdE to stochastic epidemic models. Optimal designs evaluated using ABCdE are compared to those evaluated using existing methods for the stochastic death and susceptible-infectious (SI) models. We present the Bayesian optimal experimental designs for the susceptible-infectious-susceptible (SIS) model using ABCdE.