Efficient and Practical Sample Pooling High-Throughput PCR Diagnosis of COVID-19

Haran Shani-Narkiss; Omri David Gilday; Nadav Yayon; Itamar Daniel Landau

However, what happens when the frequency of positive samples (p) rises? It is clear that a strategy of pooling 100 samples together will not be beneficial for higher p, say 0.2. In this case, we can be certain that every batch will show positive, and therefore the chance that the pooling will yield any information at all is essentially zero. In practice the frequency of positive samples has varied greatly from country to country, depending on the criterion for testing and the stage of the pandemic at time of testing. For example, as of Therefore, we set out to find a solution to the following problem: what is the optimal batch size for pooling samples, for any given p. We do this with two alternative approaches. The first approach calls for repeated pooling in several stages, and may be harder to implement in everyday lab work. However, it offers dramatic reductions in the number of tests required for very low values of p, and might be very useful for more advanced laboratories focusing on surveys of asymptomatic populations, where the appearance of positive examples is expected to be low. The second approach was in fact . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.