10.1371/journal.pcbi.1014252">
 

Document Type

Article

Publication Date

5-8-2026

Abstract

Protecting public health from infectious diseases requires collective action, as individual behaviors—such as vaccination and mask-wearing—directly influence disease dynamics. During the COVID-19 pandemic, unexpected public responses often undermined the effectiveness of interventions, highlighting the need to understand collective behavioral patterns and motivations to design more effective mitigation strategies. This study presents an agent-based simulation model that captures how individuals adjust self-protective behaviors based on evolving opinions about disease risk and examines how these decisions interact with external factors, such as public health interventions, to shape collective outcomes. To improve the representativeness of the simulated population, multiple datasets were integrated to generate artificial populations. Model behavior was then calibrated against selected observed patterns, acknowledging that this calibration is partial and subject to model assumptions. Simulation experiments were conducted under varying conditions, including optional non-pharmaceutical interventions (NPIs), heterogeneous responses to pro- and anti-intervention messaging, and alternative vaccine eligibility policies. Results suggest that decision-making patterns may vary across demographic groups and that interactions between individual behaviors and external influences can substantially affect disease dynamics within the model. The simulation framework reproduces patterns consistent with observed phenomena, such as reduced mask-wearing following the lifting of mandates for vaccinated populations and higher infection burdens among economically disadvantaged populations. The framework suggests plausible mechanisms through which such patterns may emerge under the model assumptions. The study also suggests subpopulations likely to experience greater pressure under NPI mandates and highlights how vaccine eligibility strategies may enhance disease control when behavioral responses are considered. Overall, this work underscores the importance of integrating behavioral dynamics into epidemic response planning, while recognizing that findings are contingent on model structure and assumptions.

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© 2026 The Authors. 

This article is published by PLoS, licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Source Publication

PLOS Computational Biology (ISSN 1553-7358)

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