Group testing via hypergraph factorization applied to COVID-19

David Hong, Rounak Dey, Xihong Lin (), Brian Cleary () and Edgar Dobriban ()
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David Hong: University of Pennsylvania
Rounak Dey: Harvard T.H. Chan School of Public Health
Xihong Lin: Harvard T.H. Chan School of Public Health
Brian Cleary: Broad Institute of MIT and Harvard
Edgar Dobriban: University of Pennsylvania

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Large scale screening is a critical tool in the life sciences, but is often limited by reagents, samples, or cost. An important recent example is the challenge of achieving widespread COVID-19 testing in the face of substantial resource constraints. To tackle this challenge, screening methods must efficiently use testing resources. However, given the global nature of the pandemic, they must also be simple (to aid implementation) and flexible (to be tailored for each setting). Here we propose HYPER, a group testing method based on hypergraph factorization. We provide theoretical characterizations under a general statistical model, and carefully evaluate HYPER with alternatives proposed for COVID-19 under realistic simulations of epidemic spread and viral kinetics. We find that HYPER matches or outperforms the alternatives across a broad range of testing-constrained environments, while also being simpler and more flexible. We provide an online tool to aid lab implementation: http://hyper.covid19-analysis.org .

Date: 2022
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DOI: 10.1038/s41467-022-29389-z

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