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Calibrating complex epidemiological models to observed data is a crucial stepto provide both insights into the current disease dynamics, i.e.\ by estimatinga reproductive number, as well as to provide reliable forecasts and scenarioexplorations. Here we present a new approach to calibrate an agent-based model-- EpiCast -- using a large set of simulation ensembles for different majormetropolitan areas of the United States. In particular, we propose: a newneural network based surrogate model able to simultaneously emulate alldifferent locations; and a novel posterior estimation that provides not onlymore accurate posterior estimates of all parameters but enables the jointfitting of global parameters across regions.

Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates