Calibrating a Stochastic, Agent-Based Model Using Quantile-Based Emulation

Abstract

In a number of cases, the quantile Gaussian process has proven effective in emulating stochastic, univariate computer model output [M. Plumlee and R. Tuo, Technometrics, 56 (2014), pp. 466--473]. In this paper, we develop an approach that uses this emulation approach within a Bayesian model calibration framework to calibrate an agent-based model of an epidemic. In addition, this approach is extended to handle the multivariate nature of the model output, which gives a time series of the count of infected individuals. The basic modeling approach is adapted from [D. Higdon et al., J. Amer. Statist. Assoc., 103 (2008), pp. 570--583], using a basis representation to capture the multivariate model output. The approach is motivated with an example taken from the 2015 Ebola challenge workshop which simulated an Ebola epidemic to evaluate methodology.