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Zhong Y, Xu P, Zhong S, Ding J. A sequential decoding procedure for pooled quantitative measure. Seq Anal 2022. [DOI: 10.1080/07474946.2022.2043049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yunning Zhong
- School of Mathematics and Statistics, Fujian Normal University, Fuzhou, Fujian, China
| | - Ping Xu
- School of Mathematics and Statistics, Guangxi Normal University, Guilin, Guangxi, China
| | - Siming Zhong
- School of Mathematics and Statistics, Guangxi Normal University, Guilin, Guangxi, China
| | - Juan Ding
- School of Mathematics and Statistics, Guangxi Normal University, Guilin, Guangxi, China
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Cleary B, Hay JA, Blumenstiel B, Harden M, Cipicchio M, Bezney J, Simonton B, Hong D, Senghore M, Sesay AK, Gabriel S, Regev A, Mina MJ. Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings. Sci Transl Med 2021; 13:eabf1568. [PMID: 33619080 PMCID: PMC8099195 DOI: 10.1126/scitranslmed.abf1568] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 02/10/2021] [Indexed: 12/17/2022]
Abstract
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.
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Affiliation(s)
- Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - James A Hay
- Centre for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | | | - Maegan Harden
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Jon Bezney
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Brooke Simonton
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - David Hong
- Wharton Statistics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Madikay Senghore
- Centre for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Abdul K Sesay
- Medical Research Council Unit The Gambia at London School of Hygiene and Tropical Medicine, P.O. Box 273, Banjul, The Gambia
| | - Stacey Gabriel
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Michael J Mina
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
- Centre for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02120, USA
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Cleary B, Hay JA, Blumenstiel B, Harden M, Cipicchio M, Bezney J, Simonton B, Hong D, Senghore M, Sesay AK, Gabriel S, Regev A, Mina MJ. Using viral load and epidemic dynamics to optimize pooled testing in resource constrained settings. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2020.05.01.20086801. [PMID: 32511487 PMCID: PMC7273255 DOI: 10.1101/2020.05.01.20086801] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Extensive virological testing is central to 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 combine 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 losses in sensitivity against the time course of individual infections. Using this framework, we show that prevalence can be accurately estimated across four orders of magnitude using only a few dozen pooled tests without the need for individual identification. We then exhaustively evaluate 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 positives compared to individual testing with a given budget. We illustrate how pooling affects sensitivity and overall detection capacity during an epidemic and on each day post infection, finding that sensitivity loss is mainly attributed to individuals sampled at the end of infection when detection for public health containment has minimal benefit. Crucially, we confirm that our theoretical results can be accurately translated into practice using pooled human nasopharyngeal specimens. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect epidemiologically relevant infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings.
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Affiliation(s)
- Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - James A. Hay
- Centre for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard School of Public Health
| | | | - Maegan Harden
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | | | - Jon Bezney
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | | | - David Hong
- Wharton Statistics, University of Pennsylvania, Philadelphia, PA, USA
| | - Madikay Senghore
- Centre for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - Abdul K. Sesay
- Medical Research Council Unit The Gambia at London School of Hygiene and Tropical Medicine, PO Box 273, Banjul, The Gambia
| | - Stacey Gabriel
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142 USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Michael J. Mina
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
- Centre for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard School of Public Health
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School
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