Optimal Mitigation of SIR Epidemics Under Model Uncertainty

2022/09/03 by Baike She, She, Baike, Shreyas Sundaram +3
Mathematics · Medicine · #COVID-19 epidemiological studies #FOS: Electrical engineering #Mathematical and Theoretical Epidemiology and Ecology Models #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2209.01508

openalex publication_date 2022/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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