Control of SIR Epidemics: Sacrificing Optimality for Feasibility

TL;DR

This paper investigates a robust control approach for epidemic mitigation using SIR models, focusing on feasibility under parameter estimation errors and measurement uncertainties, and sacrificing some optimality to ensure system stability.

Contribution

It introduces a parameter estimation method, characterizes estimation errors, and proposes a robust control strategy that prioritizes feasibility over optimality in epidemic control.

Findings

Robust control can maintain epidemic suppression despite estimation errors.

Overestimating epidemic severity ensures system feasibility.

Sacrificing some optimality improves the robustness of epidemic mitigation.

Abstract

We study the impact of parameter estimation and state measurement errors on a control framework for optimally mitigating the spread of epidemics. We capture the epidemic spreading process using a susceptible-infected-removed (SIR) epidemic model and consider isolation as the control strategy. We use a control strategy to remove (isolate) a portion of the infected population. Our goal is to maintain the daily infected population below a certain level, while minimizing the resource captured by the isolation rate. Distinct from existing works on leveraging control strategies in epidemic spreading, we propose a parameter estimation strategy and further characterize the parameter estimation error bound. In order to deal with uncertainties, we propose a robust control strategy by overestimating the seriousness of the epidemic and study the feasibility of the system. Compared to the optimal control strategy, we establish that the proposed strategy under parameter estimation and measurement errors will sacrifice optimality to flatten the curve.