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. 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. 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. Virological testing is central to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) containment, but many settings face severe limitations on testing.
Ninety-six–sample pooling template (Excel). RT-qPCR results for pooling validations.ĭata file S1. Description of all parameters used in the viral kinetics and transmission models. Pool design for combinatorial test with 96 samples. Positive sample distribution within validation pools. Ct values from qPCR on pooled samples with variable viral load. List of all group test designs for sample identification. qPCR calibration curve using standard viral RNA copies. Markov chain Monte Carlo trace plots from fitting to swab and sputum data.įig. Posterior distributions of estimated parameters fitted to swab and sputum data.įig.
Evaluation of pooled testing for sample identification in the multiwave epidemic shown in fig. Evaluation of pooled testing in a sustained, multiwave epidemic.įig. Effectiveness of optimal testing design under resource constraints using sputum data.įig. Effectiveness of optimal testing design under resource constraints at high prevalence.įig. Group testing for sample identification during epidemic decline.įig. Sensitivity of sample identification relative to dilution factor and time since peak viral load.įig. Prevalence estimation can depend on training and application period.įig. True prevalence against maximum likelihood estimates.įig. cgi/content/full/scitranslmed.abf1568/DC1įig.
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