The Interplay Between Individual Mobility, Health Risk, and Economic Choice: A Holistic Model for COVID-19 Policy Intervention

Zihao Yang (), Ramayya Krishnan () and Beibei Li ()
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Zihao Yang: Information Systems and Management, Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Ramayya Krishnan: Information Systems and Management, Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Beibei Li: Information Systems and Management, Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

INFORMS Joural on Data Science, 2024, vol. 3, issue 1, 6-27

Abstract: This paper was motivated by the need to simultaneously address two competing policy objectives during the course of the COVID pandemic: namely, the public health objective, which required people to be less mobile, and the economic objective, which aimed to ensure that the economy was not adversely affected by the constraints imposed by the first objective. To realize these objectives, we developed a data-informed approach to model human mobility, health risk, and economic activity jointly. This approach computes equilibrium between epidemic models of public health and economic activity under policy interventions that could be used to change people’s mobility behavior. Our approach is distinctive in its capacity to assemble proprietary data sets from public and private sectors at the individual and the zip code levels, which heretofore had not been used together. These data enabled customization of the population-level epidemic models widely used in public health (e.g., the SIR model) with individual-level data traces of mobility behaviors for assessment of public health risks. The outputs of the proposed model enabled parameterization of economic choice models of individuals’ economic decision-making. Various policy interventions and their capacities to shift the equilibrium between economic activity and public health were investigated in this study. Whereas the data-informed joint modeling approach was developed and tested in the pandemic context, it is generalizable for the evaluation of any counterfactual policy interventions.

Keywords: mobility analytics; pandemic policy design; economic choice; consumer location data; healthcare risk; epidemic modeling; data-driven policy making (search for similar items in EconPapers)
Date: 2024
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