Optimal Mitigation of SIR Epidemics Under Model Uncertainty

TL;DR

This paper investigates how to optimally mitigate SIR epidemic spread under uncertain model parameters by proposing a testing-based control strategy that overestimates epidemic severity, balancing effectiveness and resource use.

Contribution

It introduces a novel testing strategy that accounts for model uncertainty, demonstrating its effectiveness in epidemic mitigation compared to traditional optimal strategies.

Findings

The proposed strategy flattens the epidemic curve effectively.

It requires more tests than the optimal strategy.

It extends the duration of mitigation efforts.

Abstract

We study the impact of model parameter uncertainty on optimally mitigating the spread of epidemics. We capture the epidemic spreading process using a susceptible-infected-removed (SIR) epidemic model and consider testing for isolation as the control strategy. We use a testing 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 total number of tests. Distinct from existing works on leveraging control strategies in epidemic spreading, we propose a testing strategy by overestimating the seriousness of the epidemic and study the feasibility of the system under the impact of model parameter uncertainty. Compared to the optimal testing strategy, we establish that the proposed strategy under model parameter uncertainty will flatten the curve effectively but require more tests and a longer time period.