  In a number of cases, the Quantile Gaussian Process (QGP) has proven
effective in emulating stochastic, univariate computer model output (Plumlee
and Tuo, 2014). 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 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.
