Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings
Name
eabf1568.full.pdf
Description
Published version
Size
4.36 MB
Format
Adobe PDF
Checksum (MD5)
bb5b97a7b2322ff972e5caad6f339a6f
Author(s)
Cleary, Brian Lowman
Hay, James A.
Blumenstiel, Brendan
Harden, Maegan
Cipicchio, Michelle
Bezney, Jon
Simonton, Brooke
Hong, David
Senghore, Madikay
Sesay, Abdul K.
Date Issued
February 2021
Journal
Science Translational Medicine
Publisher
American Association for the Advancement of Science (AAAS)
Citation
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
Version
Final published version
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.
MIT Department
Broad Institute of MIT and Harvard
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1126/scitranslmed.abf1568
