On the adoption of nonpharmaceutical interventions during the pandemic: An evolutionary game model

Author:

Wei Zhiyuan1,Zhuang Jun1ORCID

Affiliation:

1. Department of Industrial and Systems Engineering University at Buffalo Buffalo New York USA

Abstract

AbstractThe adoption of behavioral nonpharmaceutical interventions (NPIs) among the public is essential for tackling the COVID‐19 pandemic, yet presents challenges due to the complexity of human behaviors. A large body of literature has utilized classic game theory to investigate the population's decisions regarding the adoption of interventions, where the static solution concept such as the Nash equilibrium is studied. However, individual adoption behavior is not static, instead it is a dynamic process that involves the strategic interactions with other counterparts over time. The study of quantitatively analyzing the dynamics on precautionary behavior during an outbreak is rather scarce. This article fills the research gap by developing an evolutionary game‐theoretic framework to model the dynamics of population behavior on the adoption of NPI. We construct the two‐group asymmetric game, where behavioral change for each group is characterized by replicator equations. Sensitivity analyses are performed to examine the long‐term stability of equilibrium points with respect to perturbation of model parameters. We found that the limiting behavior of intervention adoption in the population consists of only pure strategies in a game setting, indicating that the evolutionary outcome is that everyone either takes up the preventive measure or not. We also applied the framework to examine the mask‐wearing behavior, and validated with actual data. Overall, this article provides insights into population dynamics on the adoption of intervention strategy during the outbreak, which can be beneficial for policy makers to better understand the evolutionary trajectory of population behavior.

Publisher

Wiley

Subject

Physiology (medical),Safety, Risk, Reliability and Quality

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