Using viral loads and epidemic dynamics to perform the best test in resource-limited conditions

This article was originally published here

Sci Transl Med. 2021 February 22: eabf1568. doi: 10.1126 / scitranslmed.abf1568. Online ahead of print.


Ornithological testing is at the heart of severe respiratory control of coronavirus 2 (SARS-CoV-2), but many conditions meet real limitations on testing. Group experiments offer a way to increase throughput by testing pools of samples together; however, most of the proposed designs have not yet addressed key concerns about loss of sensitivity and feasibility of implementation. Here, we combined a mathematical model of epidemic transmission and empirically derived viral kinetics for SARS-CoV-2 diseases to identify a pool design that is robust to changes in frequency, and to confirm loss of sensitivity against a time course of individual diseases. We show that frequency can be measured accurately over a wide range, from 0.02% to 20%, using just a few dozen tests combined, and using up to 400 times fewer tests than would be required for identification. separately. We then conducted a complete evaluation of the ability of different pool designs to detect the number of detected diseases under different resource constraints, finding that a simple pool design can test up to 20 times as many advanced objects as the individual experiments with dedicated budget. We show how a swimming pool affects sensitivity and overall detection ability during a pandemic and on each day after an infection, finding that only 3% of false negative tests occurred when individuals are sampled in the first week of infection after the peak load, and loss of sensitivity is largely due to individuals sampled at the end of infection when a detection is found to be limiting the transmission of the minimum gain. Crucially, we demonstrated that our theoretical results can be translated into practice using accurate human nasopharyngeal samples by accurately estimating a frequency of 1% among 2,304 samples using only 48 tests, and by sample identification. collected in a panel of 960 samples. Our findings show that reporting a difference in sampled viral loads provides a positive picture of how aggregation affects sensitivity to detect diseases. Using simple, practical group test designs can significantly increase study capabilities in resource-limited situations.

PMID: 33619080 | DOI: 10.1126 / scitranslmed.abf1568