Data-Driven Modeling to Facilitate Policymaking in Fighting to Contain the COVID-19 Pandemic

Qiu, R. G.; Wang, E. H.; Gong, I.

This study shows how data-driven modeling can be applied to facilitating policymaking at the geographical hierarchy in terms of the administrative structure of regions and communities when a public health crisis arises. Specifically, rich data and machine learning based models are explored for public health policies, exploring the timing and restrictive levels of intervention measures, such as school/workplace closure and lifting, gathering ban, or travel restrictions, needed for a community, at the region and community level as time goes. This study articulates that rich data and machine learning work well in reducing policy discrepancies. Real world data of COVID-19 cases at the state level in the U.S. are used first in this study to show the consequence of different policy responses in 2020. To demonstrate what different policy responses could result, an agent-based simulation model using a small-scale school setting will be then presented. The simulation model could be further developed, scaled up, and customarily adopted across any geographical hierarchy, facilitating policymaking in public health. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference, June 2021.