Pooled RNA extraction and PCR assay for efficient SARS-CoV-2 detection

Roni Ben-Ami; Agnes Klochendler; Matan Seidel; Tal Sido; Ori Gurel-Gurevich; Moran Yassour; Eran Meshorer; Gil Benedek; Irit Fogel; Esther Oiknine-Djian; Asaf Gertler; Zeev Rotstein; Bruno Lavi; Yuval Dor; Dana G Wolf; Maayan Salton; Yotam Drier

Group testing is a field of research in the intersection of mathematics, computer science and information theory, with applications in biology, communication and more. A group testing algorithm is a testing scheme which is directed towards minimizing the number of tests conducted on a set of samples by using the ability to test pooled subsets of samples. If a pool of n samples tests negative, all samples must be negative, and therefore their status has been determined in only one test instead of n individual tests. Various group testing algorithms exist, with different assumptions and constraints (see [14, 15] ). While many such algorithms, most notably binary splitting, may be very efficient in theory, they might be unsuitable because of practical limitations. Three such limitations might be: (1) a limit on the number of stages due to the importance of delivering a test result quickly, exemplified by the urgent clinical context of COVID-19 diagnosis; (2) a limit on the ability to dilute samples and still safely identify a single positive sample in a pool; (3) favorability of simple algorithms which may minimize human error in a laboratory setting.