Group testing via hypergraph factorization applied to COVID-19

David Hong; Rounak Dey; Xihong Lin; Brian Cleary; Edgar Dobriban

doi:10.1038/s41467-022-29389-z

Nature Communications (Apr 2022)

Group testing via hypergraph factorization applied to COVID-19

David Hong,
Rounak Dey,
Xihong Lin,
Brian Cleary,
Edgar Dobriban

Affiliations

David Hong: Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
Rounak Dey: Department of Biostatistics, Harvard T.H. Chan School of Public Health
Xihong Lin: Department of Biostatistics, Harvard T.H. Chan School of Public Health
Brian Cleary: Broad Institute of MIT and Harvard
Edgar Dobriban: Department of Statistics and Data Science, The Wharton School, University of Pennsylvania

DOI: https://doi.org/10.1038/s41467-022-29389-z
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 13

Abstract

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This paper proposes HYPER, a method for screening more people using fewer tests by testing pools formed via hypergraph factorization. HYPER is not only efficient but is also simple to implement, flexible, and has maximally balanced pools.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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