  Computational models help decision makers understand epidemic dynamics to
optimize public health interventions. Agent-based simulation of disease spread
in synthetic populations allows us to compare and contrast different effects
across identical populations or to investigate the effect of interventions
keeping every other factor constant between ``digital twins''. FRED (A
Framework for Reconstructing Epidemiological Dynamics) is an agent-based
modeling system with a geo-spatial perspective using a synthetic population
that is constructed based on the U.S. census data. In this paper, we show how
Gaussian process regression can be used on FRED-synthesized data to infer the
differing spatial dispersion of the epidemic dynamics for two disease
conditions that start from the same initial conditions and spread among
identical populations. Our results showcase the utility of agent-based
simulation frameworks such as FRED for inferring differences between conditions
where controlling for all confounding factors for such comparisons is next to
impossible without synthetic data.
