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A Partition-Based Group Testing Algorithm for Estimating the Number of Infected Individuals
Beigel, Richard Webber, Max J.
Beigel, Richard
Webber, Max J.
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Pre-print
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2021-07-28
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Computer and Information Sciences
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https://doi.org/10.1101/2021.07.27.21260924
Abstract
The dangers of COVID-19 remain ever-present worldwide. The asymptomatic nature of COVID-19 obfuscates the signs policy makers look for when deciding to reopen public areas or further quarantine. In much of the world, testing resources are often scarce, creating a need for testing potentially infected individuals that prioritizes efficiency. This report presents an advancement to Beigel and Kasif’s Approximate Counting Algorithm (ACA). ACA estimates the infection rate with a number of tests that is logarithmic in the population size. Our newer version of the algorithm provides an extra level of efficiency: each subject is tested exactly once. A simulation of the algorithm, created for and presented as part of this paper, can be used to find a linear regression of the results with R2 > 0.999. This allows stakeholders and members of the biomedical community to estimate infection rates for varying population sizes and ranges of infection rates.
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Richard Beigel, Max J. Webber. A Partition-Based Group Testing Algorithm for Estimating the Number of Infected Individuals. medRxiv 2021.07.27.21260924; doi: https://doi.org/10.1101/2021.07.27.21260924
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