Recent reports suggest that 10-30% of SARS-CoV-2 infected patients are asymptomatic and that significant viral shedding may occur prior to symptom onset. Therefore, there is an urgent need to increase diagnostic testing capabilities to prevent disease spread. We developed P-BEST - a method for Pooling-Based Efficient SARS-CoV-2 Testing which identifies all positive subjects within a large set of samples using a single round of testing. Each sample is assigned into multiple pools using a combinatorial pooling strategy based on compressed sensing designed for maximizing carrier detection. In our current study we pooled sets of 384 samples into 48 pools providing both an 8-fold increase in testing efficiency, as well as an 8-fold reduction in test costs. We successfully identified up to 5 positive carriers within sets of 384 samples. We then used P-BEST to screen 1115 healthcare workers using 144 tests. P-BEST provides an efficient and easy-to-implement solution for increasing testing capacity that can be easily integrated into diagnostic laboratories.

We study the problem of generating a large sample from a data stream S of elements (i, v), where i is a positive integer key, v is an integer equal to the count of key i, and the sample consists of pairs (i, C i ) for C i = (i,v)∈S v. We consider strict turnstile streams and general non-strict turnstile streams, in which C i may be negative. Our sample is useful for approximating both forward and inverse distribution statistics, within an additive error and provable success probability 1 − δ.Our sampling method improves by an order of magnitude the known processing time of each stream element, a crucial factor in data stream applications, thereby providing a feasible solution to the sampling problem. For example, for a sample of size O ( −2 log (1/δ)) in non-strict streams, our solution requires O ((log log(1/ )) 2 + (log log(1/δ)) 2 ) operations per stream element, whereas the best previous solution requires O ( −2 log 2 (1/δ)) evaluations of a fully independent hash function per element. We achieve this improvement by constructing an efficient K -elements recovery structure from which K elements can be extracted with probability 1 − δ. Our structure enables our sampling algorithm to run on distributed systems and extract statistics on the difference between streams.

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The SARS-CoV-2 pandemic led to unprecedented testing demands, causing significant testing delays globally. One strategy used for increasing testing capacity was pooled-testing, using a two-stage technique first introduced during WWII. Here we report the development, validation and clinical application of P-BEST - a single-stage pooled-testing strategy that was approved for clinical use in Israel. P-BEST was clinically evaluated using 3,636 side-by-side tests and was able to correctly detect all positive samples and accurately estimate their Ct value. P-BEST was then used to clinically test 837,138 samples using 270,095 PCR tests - a 3.1 fold reduction in the number of tests. Importantly, P-BEST was also used during the Alpha and Delta waves, when positivity rates exceeded 10%, rendering traditional pooling non-practical. We also describe a tablet-based solution that allows performing manual single-stage pooling in settings where liquid dispensing robots are not available. Our data provides a proof-of-concept for large-scale clinical implementation of single-stage pooled-testing for continuous surveillance of multiple pathogens with reduced test costs, and as an important tool for increasing testing efficiency during pandemic outbreaks.