Stay Home or Not? Modeling Individuals’ Decisions During the COVID-19 Pandemic

Abstract

During the COVID-19 pandemic, staying home proved to be an effective way to mitigate the spread of the virus. Stay-at-home orders and guidelines were issued by governments across the globe and were followed by a large portion of the population in the early stages of the outbreak when there was a lack of COVID-specific medical knowledge. The decision of whether to stay home came with many trade-offs, such as risking personal exposure to the virus when leaving home or facing financial and mental health burdens when remaining home. In this research, we study how individuals make strategic decisions to balance these conflicting outcomes. We present a model to study individuals’ decision making based on decision and prospect theory, and we conduct sensitivity analysis to study the fluctuations in optimal strategies when there are changes made to the model’s parameters. A Monte Carlo simulation is implemented to further study the performance of our model, and we compare our simulation results with real data that captures individuals’ stay-at-home decisions. Overall, this research models and analyzes the behaviors of individuals during the COVID-19 pandemic and can help support decision making regarding control measures and policy development when public health emergencies appear in the future. History: This article was accepted for the Decision Analysis Special Issue on Emerging Topics in Health. Funding: The first two authors’ efforts were supported by the National Natural Science Foundation of China [Grants 71971217 and 71671189], the Key Project of Natural Science Foundation of China [Grants 71790615 and 91846301], and the Independent Exploration of Innovation Project for Postgraduate of Central South University [Grant 2019zzts843]. The third author’s effort was partially supported by the U.S. National Science Foundation Graduate Research Fellowship Program [Grant 2043091].