2021-10-252021-10-252021-07-28Richard 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/10.1101/2021.07.27.21260924http://dx.doi.org/10.34944/dspace/6969http://hdl.handle.net/20.500.12613/6988The 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.8 pagesengAttribution-NoDerivs CC BY-NDhttp://creativecommons.org/licenses/by-nd/4.0/EpidemiologyText