  Agent-based models (ABM) provide an excellent framework for modeling
outbreaks and interventions in epidemiology by explicitly accounting for
diverse individual interactions and environments. However, these models are
usually stochastic and highly parametrized, requiring precise calibration for
predictive performance. When considering realistic numbers of agents and
properly accounting for stochasticity, this high dimensional calibration can be
computationally prohibitive. This paper presents a random forest based
surrogate modeling technique to accelerate the evaluation of ABMs and
demonstrates its use to calibrate an epidemiological ABM named CityCOVID via
Markov chain Monte Carlo (MCMC). The technique is first outlined in the context
of CityCOVID's quantities of interest, namely hospitalizations and deaths, by
exploring dimensionality reduction via temporal decomposition with principal
component analysis (PCA) and via sensitivity analysis. The calibration problem
is then presented and samples are generated to best match COVID-19
hospitalization and death numbers in Chicago from March to June in 2020. These
results are compared with previous approximate Bayesian calibration (IMABC)
results and their predictive performance is analyzed showing improved
performance with a reduction in computation.
