No Panic in Pandemic: The Impact of Individual Choice on Public Health Policy

Miao Bai (), Ying Cui (), Guangwen Kong () and Anthony Zhenhuan Zhang ()
Additional contact information
Miao Bai: Department of Operations and Information Management, University of Connecticut, Storrs, Connecticut 06269
Ying Cui: Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720
Guangwen Kong: Department of Statistics, Operations and Data Science, Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122
Anthony Zhenhuan Zhang: Meta Platform, Inc., Menlo Park, California 94025

Manufacturing & Service Operations Management, 2024, vol. 26, issue 5, 1622-1638

Abstract: Problem definition : Public health interventions, such as social distancing and lockdown, play an important role in containing infectious disease outbreaks, such as coronavirus disease 2019 (COVID-19). Yet, these interventions could cause significant financial losses because of the disruption to regular socioeconomic activities. Moreover, an individual’s activity level is influenced not only by public health policies but also by one’s perception of the disease burden of infection. Strategic planning is required to optimize the timing and intensity of these public health interventions by considering individual responses. Methodology/results : We use the multinomial logit choice model to characterize individual reactions to the risk of infection and public health interventions and integrate it into a repeated Stackelberg game with the susceptible-infected-recovered disease transmission dynamics. We find that the individual equilibrium activity level is higher than the socially optimal activity level because of an individual’s ignorance of the negative externality imposed on others. As a result, implementing lockdown and social distancing policies at moderate disease prevalence may be equivalently critical, if not more, compared with their implementations when the disease prevalence is at its peak level. To verify these findings, we conduct numerical studies based on representative COVID-19 data in Minnesota. Managerial implications : Our results call for policymakers’ attention to consider the impact of individuals’ responses in the planning for different pandemic containment measures. Individuals’ responses in the pandemic may significantly affect the optimal implementation of lockdown and social distancing policies.

Keywords: COVID-19; dynamic compartmental model; public health policy analysis; game theory; logit model (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2022.0514 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:5:p:1622-1638

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().