Calibrating complex epidemiological models to observed data is a crucial step to provide both insights into the current disease dynamics, i.e.\ by estimating a reproductive number, as well as to provide reliable forecasts and scenario explorations. Here we present a new approach to calibrate an agent-based model -- EpiCast -- using a large set of simulation ensembles for different major metropolitan areas of the United States. In particular, we propose: a new neural network based surrogate model able to simultaneously emulate all different locations; and a novel posterior estimation that provides not only more accurate posterior estimates of all parameters but enables the joint fitting of global parameters across regions.
@article{arxiv.2010.06558,
title = {Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates},
author = {Rushil Anirudh and Jayaraman J. Thiagarajan and Peer-Timo Bremer and Timothy C. Germann and Sara Y. Del Valle and Frederick H. Streitz},
journal= {arXiv preprint arXiv:2010.06558},
year = {2020}
}