Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings

Abstract

Simple group testing designs to improve SARS-CoV-2 surveillance in resource-constrained settings are identified using modeling and experimental data. Batch testing for SARS-CoV-2 Frequent and accurate RT-PCR–based testing is essential for preventing and managing SARS-CoV-2 infection; however, active infection surveillance is still often limited by time or resources. Cleary et al. demonstrate that considering population-level viral prevalence and individual viral loads allows for efficiency gains upon pooled testing with minimal loss of sensitivity, both theoretically and as validated in vitro using human swab and sputum samples. Barak et al. show that pooled testing of 133,816 hospital-collected patient nasopharyngeal samples eliminated three quarters of testing reactions with only a minor reduction in sensitivity, demonstrating the efficacy of the approach in the field. Both studies suggest that considered pooling of individual samples before testing could reliably increase SARS-CoV-2 testing throughput. 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.