Game-theoretic modeling of collective decision-making during epidemics

TL;DR

This paper introduces a game-theoretic model capturing the co-evolution of individual behavior and epidemic spread on networks, enabling analysis of collective responses, oscillations, and policy impacts.

Contribution

It presents a novel, unified framework that models long-term human behavior and epidemic dynamics, addressing limitations of previous reactive models.

Findings

The model predicts emergent phenomena like oscillations and resurgences.

It assesses policy effectiveness in promoting collective epidemic eradication.

Case studies demonstrate real-world applicability.

Abstract

The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral--epidemic model, in which an interplay of realistic factors shapes the co-evolution of individual decision-making and epidemics on a network. Although such a co-evolution is deeply intertwined in the real-world, existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our model offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks. The framework also allows to assess the effectiveness of different policy interventions on ensuring a collective response that successfully eradicates the outbreak. Two case studies, inspired by real-world diseases, are presented to illustrate the potentialities of the proposed model.