Adaptive group testing in a compartmental model of COVID-19<sup>*</sup>

Tamás Tekeli; Attila Dénes; Gergely Röst; Tamás Tekeli; Attila Dénes; Gergely Röst

doi:10.3934/mbe.2022513

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 11: 11018-11033. doi: 10.3934/mbe.2022513

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Adaptive group testing in a compartmental model of COVID-19^*

Bolyai Institute, University of Szeged, Aradi vértanúk tere 1., H-6720 Szeged, Hungary
Dedicated to the memory of Stephen Gourley

Received: 31 December 2021 Revised: 22 April 2022 Accepted: 05 July 2022 Published: 02 August 2022

Various measures have been implemented around the world to prevent the spread of SARS-CoV-2. A potential tool to reduce disease transmission is regular mass testing of a high percentage of the population, possibly with pooling (testing a compound of several samples with one single test). We develop a compartmental model to study the applicability of this method and compare different pooling strategies: regular and Dorfman pooling. The model includes isolated compartments as well, from where individuals rejoin the active population after some time delay. We develop a method to optimize Dorfman pooling depending on disease prevalence and establish an adaptive strategy to select variable pool sizes during the course of the epidemic. It is shown that optimizing the pool size can avert a significant number of infections. The adaptive strategy is much more efficient, and may prevent an epidemic outbreak even in situations when a fixed pool size strategy can not.
- mass testing,
- pooling,
- delay differential equation,
- COVID-19,
- adaptive strategy,
- surveillance
Citation: Tamás Tekeli, Attila Dénes, Gergely Röst. Adaptive group testing in a compartmental model of COVID-19*[J]. Mathematical Biosciences and Engineering, 2022, 19(11): 11018-11033. doi: 10.3934/mbe.2022513

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Abstract

Various measures have been implemented around the world to prevent the spread of SARS-CoV-2. A potential tool to reduce disease transmission is regular mass testing of a high percentage of the population, possibly with pooling (testing a compound of several samples with one single test). We develop a compartmental model to study the applicability of this method and compare different pooling strategies: regular and Dorfman pooling. The model includes isolated compartments as well, from where individuals rejoin the active population after some time delay. We develop a method to optimize Dorfman pooling depending on disease prevalence and establish an adaptive strategy to select variable pool sizes during the course of the epidemic. It is shown that optimizing the pool size can avert a significant number of infections. The adaptive strategy is much more efficient, and may prevent an epidemic outbreak even in situations when a fixed pool size strategy can not.

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