Calibrating a Stochastic Agent Based Model Using Quantile-based Emulation

In a number of cases, the Quantile Gaussian Process(QGP) has proven effective in emulatingstochastic, univariate computer model output (Plumlee and Tuo, 2014). In this paper, we develop an approach that uses this emulationapproach within a Bayesian model calibration framework to calibrate an agent-based modelof 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 Higdon et al. (2008), 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.