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

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. W

SUBMITTER: Cleary B 

PROVIDER: S-EPMC7273255 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature