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

• URL: http://arxiv.org/abs/2010.06558v1
• Date: Tue, 13 Oct 2020 17:19:30 GMT
• Title: Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates
• Authors: Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz
• Abstract summary: 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.
• Score: 33.88734751290751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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