What is it about?
Due to the emergence of COVID-19, pooled testing has gained significant attention as a method for allocating testing resources more efficiently. Aiming to identify infected individuals in a group of people with as few tests as possible, the most common strategy for pooled testing is simple; take samples from all the people in the group, separate them into pools, mix the samples of each pool, and perform one test for each mixed sample. If the mixed sample of a pool turns out positive, retest all the initial samples of the people in that pool individually. Otherwise, classify everyone in the pool as negative. In that context, we propose a novel pooled testing method, which is specifically designed to minimize the total number of required tests, when the group of people to be tested is selected via contact tracing. Our method utilizes contextual epidemiological information at the time of testing, and we show via a variety of simulation experiments that it significantly decreases the expected number of tests, especially in the presence of strong superspreading effects.
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Why is it important?
Although there are numerous pooled testing approaches in the literature, the majority of them are agnostic to the circumstances of contagion encountered during contact tracing. However, our experience from COVID-19 has shown that, especially in the early stages of a pandemic, individuals for whom a test is ordered are usually traced contacts of an infectious person. Moreover, it has been observed that superspreading—the situation where many infect just a few but a few infect many—is a key element of COVID-19 and other infectious diseases before it. In that context, our experimental results highlight the fact that a significant amount of tests can be saved, when the dynamics of disease transmission in a community are taken into consideration during the development of the respective pooled testing strategy. This can be of particular importance in communities where the available testing resources are either too costly or severely limited.
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This page is a summary of: Pooled testing of traced contacts under superspreading dynamics, PLoS Computational Biology, March 2022, PLOS, DOI: 10.1371/journal.pcbi.1010008.
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