Effective Testing Policies for Controlling an Epidemic Outbreak

TL;DR

This paper models epidemic control through testing policies, proposing strategies to suppress or mitigate outbreaks, and evaluates their impact on ICU cases and deaths during COVID-19 in France.

Contribution

It introduces a novel epidemic model incorporating testing as a control input and devises two effective testing policies for outbreak management.

Findings

BEST policy provides a lower bound to stop epidemic growth.

COST policy minimizes peak infections with limited test resources.

Both policies significantly reduce ICU cases and deaths.

Abstract

Testing is a crucial control mechanism for an epidemic outbreak because it enables the health authority to detect and isolate the infected cases, thereby limiting the disease transmission to susceptible people, when no effective treatment or vaccine is available. In this paper, an epidemic model that incorporates the testing rate as a control input is presented. The proposed model distinguishes between the undetected infected and the detected infected cases with the latter assumed to be isolated from the disease spreading process in the population. Two testing policies, effective during the onset of an epidemic when no treatment or vaccine is available, are devised: (i) best-effort strategy for testing (BEST) and (ii) constant optimal strategy for testing (COST). The BEST is a suppression policy that provides a lower bound on the testing rate to stop the growth of the epidemic. The COST is a mitigation policy that minimizes the peak of the epidemic by providing a constant, optimal allocation of tests in a certain time interval when the total stockpile of tests is limited. Both testing policies are evaluated by their impact on the number of active intensive care unit (ICU) cases and the cumulative number of deaths due to COVID-19 in France.