Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings
Virological testing is central to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addres...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/131128 |
| _version_ | 1826217062935035904 |
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| author | Cleary, Brian Lowman Hay, James A. Blumenstiel, Brendan Harden, Maegan Cipicchio, Michelle Bezney, Jon Simonton, Brooke Hong, David Senghore, Madikay Sesay, Abdul K. Gabriel, Stacey Regev, Aviv Mina, Michael J. |
| author2 | Broad Institute of MIT and Harvard |
| author_facet | Broad Institute of MIT and Harvard Cleary, Brian Lowman Hay, James A. Blumenstiel, Brendan Harden, Maegan Cipicchio, Michelle Bezney, Jon Simonton, Brooke Hong, David Senghore, Madikay Sesay, Abdul K. Gabriel, Stacey Regev, Aviv Mina, Michael J. |
| author_sort | Cleary, Brian Lowman |
| collection | MIT |
| description | Virological testing is central to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addressed key concerns over sensitivity loss and implementation feasibility. Here, we combined a mathematical model of epidemic spread and empirically derived viral kinetics for SARS-CoV-2 infections to identify pooling designs that are robust to changes in prevalence and to ratify sensitivity losses against the time course of individual infections. We show that prevalence can be accurately estimated across a broad range, from 0.02 to 20%, using only a few dozen pooled tests and using up to 400 times fewer tests than would be needed for individual identification. We then exhaustively evaluated the ability of different pooling designs to maximize the number of detected infections under various resource constraints, finding that simple pooling designs can identify up to 20 times as many true positives as individual testing with a given budget. Crucially, we confirmed that our theoretical results can be translated into practice using pooled human nasopharyngeal specimens by accurately estimating a 1% prevalence among 2304 samples using only 48 tests and through pooled sample identification in a panel of 960 samples. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings. |
| first_indexed | 2024-09-23T16:57:22Z |
| format | Article |
| id | mit-1721.1/131128 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:57:22Z |
| publishDate | 2021 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | dspace |
| spelling | mit-1721.1/1311282022-10-03T09:24:48Z Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings Cleary, Brian Lowman Hay, James A. Blumenstiel, Brendan Harden, Maegan Cipicchio, Michelle Bezney, Jon Simonton, Brooke Hong, David Senghore, Madikay Sesay, Abdul K. Gabriel, Stacey Regev, Aviv Mina, Michael J. Broad Institute of MIT and Harvard Virological testing is central to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addressed key concerns over sensitivity loss and implementation feasibility. Here, we combined a mathematical model of epidemic spread and empirically derived viral kinetics for SARS-CoV-2 infections to identify pooling designs that are robust to changes in prevalence and to ratify sensitivity losses against the time course of individual infections. We show that prevalence can be accurately estimated across a broad range, from 0.02 to 20%, using only a few dozen pooled tests and using up to 400 times fewer tests than would be needed for individual identification. We then exhaustively evaluated the ability of different pooling designs to maximize the number of detected infections under various resource constraints, finding that simple pooling designs can identify up to 20 times as many true positives as individual testing with a given budget. Crucially, we confirmed that our theoretical results can be translated into practice using pooled human nasopharyngeal specimens by accurately estimating a 1% prevalence among 2304 samples using only 48 tests and through pooled sample identification in a panel of 960 samples. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings. National Institute of General Medical Sciences (Grant U54GM088558) 2021-07-23T21:23:04Z 2021-07-23T21:23:04Z 2021-02 2021-07-23T14:33:15Z Article http://purl.org/eprint/type/JournalArticle 1946-6234 1946-6242 https://hdl.handle.net/1721.1/131128 Cleary, Brian et al. "Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings." Science Translational Medicine 13, 589 (February 2021): eabf1568. © 2021 The Authors en 10.1126/scitranslmed.abf1568 Science Translational Medicine Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf American Association for the Advancement of Science (AAAS) Science |
| spellingShingle | Cleary, Brian Lowman Hay, James A. Blumenstiel, Brendan Harden, Maegan Cipicchio, Michelle Bezney, Jon Simonton, Brooke Hong, David Senghore, Madikay Sesay, Abdul K. Gabriel, Stacey Regev, Aviv Mina, Michael J. Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings |
| title | Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings |
| title_full | Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings |
| title_fullStr | Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings |
| title_full_unstemmed | Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings |
| title_short | Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings |
| title_sort | using viral load and epidemic dynamics to optimize pooled testing in resource constrained settings |
| url | https://hdl.handle.net/1721.1/131128 |
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