Mean-Field Game Analysis of SIR Model with Social Distancing

TL;DR

This paper models individual social distancing decisions during an epidemic using mean-field game theory, comparing selfish and socially optimal strategies, and analyzing policy implications to control disease spread effectively.

Contribution

It introduces a mean-field game framework for epidemic control, highlighting the discrepancy between individual and social strategies and quantifying the impact of policies on infection dynamics.

Findings

Infected individuals tend to make more contacts than socially optimal levels.

Cost incentives can promote socially optimal contact strategies.

Public policies should target infected individuals even after the epidemic peak.

Abstract

The current COVID-19 pandemic has proven that proper control and prevention of infectious disease require creating and enforcing the appropriate public policies. One critical policy imposed by the policymakers is encouraging the population to practice social distancing (i.e. controlling the contact rate among the population). Here we pose a mean-field game model of individuals each choosing a dynamic strategy of making contacts, given the trade-off of gaining utility but also risking infection from additional contacts. We compute and compare the mean-field equilibrium (MFE) strategy, which assumes each individual acting selfishly to maximize its own utility, to the socially optimal strategy, which maximizes the total utility of the population. We prove that the optimal decision of the infected is always to make more contacts than the level at which it would be socially optimal, which reinforces the important role of public policy to reduce contacts of the infected (e.g. quarantining, sick paid leave). Additionally, we include cost to incentivize people to change strategies, when computing the socially optimal strategies. We find that with this cost, policies reducing contacts of the infected should be further enforced after the peak of the epidemic has passed. Lastly, we compute the price of anarchy (PoA) of this system, to understand the conditions under which large discrepancies between the MFE and socially optimal strategies arise, which is when intervening public policy would be most effective.