A robust pooled testing approach to expand COVID-19 screening capacity

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dc.contributor.authorBish, Douglas R.en
dc.contributor.authorBish, Ebru K.en
dc.contributor.authorEl-Hajj, Husseinen
dc.contributor.authorAprahamian, Hrayeren
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2021-05-20T12:08:16Zen
dc.date.available2021-05-20T12:08:16Zen
dc.date.issued2021-02-08en
dc.description.abstractLimited testing capacity for COVID-19 has hampered the pandemic response. Pooling is a testing method wherein samples from specimens (e.g., swabs) from multiple subjects are combined into a pool and screened with a single test. If the pool tests positive, then new samples from the collected specimens are individually tested, while if the pool tests negative, the subjects are classified as negative for the disease. Pooling can substantially expand COVID-19 testing capacity and throughput, without requiring additional resources. We develop a mathematical model to determine the best pool size for different risk groups, based on each group's estimated COVID-19 prevalence. Our approach takes into consideration the sensitivity and specificity of the test, and a dynamic and uncertain prevalence, and provides a robust pool size for each group. For practical relevance, we also develop a companion COVID-19 pooling design tool (through a spread sheet). To demonstrate the potential value of pooling, we study COVID-19 screening using testing data from Iceland for the period, February-28-2020 to June-14-2020, for subjects stratified into high- and low-risk groups. We implement the robust pooling strategy within a sequential framework, which updates pool sizes each week, for each risk group, based on prior week's testing data. Robust pooling reduces the number of tests, over individual testing, by 88.5% to 90.2%, and 54.2% to 61.9%, respectively, for the low-risk and high-risk groups (based on test sensitivity values in the range [0.71, 0.98] as reported in the literature). This results in much shorter times, on average, to get the test results compared to individual testing (due to the higher testing throughput), and also allows for expanded screening to cover more individuals. Thus, robust pooling can potentially be a valuable strategy for COVID-19 screening.en
dc.description.notesNational Science Foundation Grant #1761842 DRB, EKB (sponsor did not play a role in study design).en
dc.description.sponsorshipNational Science FoundationNational Science Foundation (NSF) [1761842]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0246285en
dc.identifier.issn1932-6203en
dc.identifier.issue2en
dc.identifier.othere0246285en
dc.identifier.pmid33556129en
dc.identifier.urihttp://hdl.handle.net/10919/103395en
dc.identifier.volume16en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleA robust pooled testing approach to expand COVID-19 screening capacityen
dc.title.serialPlos Oneen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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Scholarly Works, Industrial and Systems Engineering
Destination Area: Global Systems Science (GSS)