Optimal Control of Behavioral-Feedback SIR Epidemic Model

TL;DR

This paper develops an optimal control framework for a behavioral-feedback SIR epidemic model, demonstrating that a threshold-based 'fill the box' strategy is optimal for minimizing intervention costs while controlling infection spread.

Contribution

It introduces a novel optimal control solution for a feedback-based epidemic model, generalizing classical SIR results to include endogenous behavioral responses.

Findings

The 'fill the box' strategy is proven optimal under general conditions.

The model accounts for voluntary social distancing and mask adoption.

The results extend classical SIR control strategies to behavioral-feedback scenarios.

Abstract

We consider a behavioral-feedback SIR epidemic model, in which the infection rate depends in feedback on the fractions of susceptible and infected agents, respectively. The considered model allows one to account for endogenous adaptation mechanisms of the agents in response to the epidemics, such as voluntary social distancing, or the adoption of face masks. For this model, we formulate an optimal control problem for a social planner that has the ability to reduce the infection rate to keep the infection curve below a certain threshold within an infinite time horizon, while minimizing the intervention cost. Based on the dynamic properties of the model, we prove that, under quite general conditions on the infection rate, the filling the box strategy is the optimal control. This strategy consists in letting the epidemics spread without intervention until the threshold is reached, then applying the minimum control that leaves the fraction of infected individuals constantly at the threshold until the reproduction number becomes less than one and the infection naturally fades out. Our result generalizes one available in the literature for the equivalent problem formulated for the classical SIR model, which can be recovered as a special case of our model when the infection rate is constant. Our contribution enhances the understanding of epidemic management with adaptive human behavior, offering insights for robust containment strategies.