期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：2
DOI：10.1073/pnas.2105180119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
Frequent mass testing can slow a rapidly spreading infectious disease by quickly identifying and isolating infected individuals from the population. One proposed method to reduce the extremely high costs of this testing strategy is to employ pooled testing, in which samples are combined and tested together using one test, and the entire pool is cleared given a negative test result. This paper demonstrates that frequent pooled testing of individuals with correlated risk—even given large uncertainty about infection rates—is particularly efficient. We conclude that frequent pooled testing using natural groupings is a cost-effective way to suppress infection risk in a pandemic.
Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by
x times only increases expected tests by around
x
. However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.
