Modeling and Control of Epidemics through Testing Policies
Muhammad Umar B. Niazi, Alain Kibangou, Carlos Canudas-de-Wit, Denis, Nikitin, Liudmila Tumash, Pierre-Alexandre Bliman

TL;DR
This paper introduces a control-theoretic epidemic model incorporating testing rates, proposing strategies to suppress or mitigate COVID-19 spread in France, and evaluates their impact on ICU cases and deaths.
Contribution
It develops a novel epidemic model with testing as a control input and proposes two testing policies, a suppression and a mitigation strategy, based on data-driven analysis.
Findings
BEST policy effectively stops epidemic growth.
COST policy minimizes peak infected population.
Both policies reduce ICU cases and deaths.
Abstract
Testing is a crucial control mechanism in the beginning phase of an epidemic when the vaccines are not yet available. It enables the public health authority to detect and isolate the infected cases from the population, thereby limiting the disease transmission to susceptible people. However, despite the significance of testing in epidemic control, the recent literature on the subject lacks a control-theoretic perspective. In this paper, an epidemic model is proposed that incorporates the testing rate as a control input and differentiates the undetected infected from the detected infected cases, who are assumed to be removed from the disease spreading process in the population. After estimating the model on the data corresponding to the beginning phase of COVID-19 in France, two testing policies are proposed: the so-called best-effort strategy for testing (BEST) and constant optimal…
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