Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers

Abstract

The COVID-19 pandemic is rapidly spreading throughout the world. Recent reports suggest that 10-30% of SARS-CoV-2 infected patients are asymptomatic. Other studies report that some subjects have significant viral shedding prior to symptom onset. Since both asymptomatic and presymptomatic subjects can spread the disease, identifying such individuals is critical for effective control of the SARS-CoV-2 pandemic. Therefore, there is an urgent need to increase diagnostic testing capabilities in order to also screen asymptomatic carriers. In fact, such tests will be routinely required until a vaccine is developed. Yet, a major bottleneck of managing the COVID-19 pandemic in many countries is diagnostic testing, due to limited laboratory capabilities as well as limited access to genome-extraction and Polymerase Chain Reaction (PCR) reagents. We developed P-BEST -a method for Pooling-Based Efficient SARS-CoV-2 Testing, using a non-adaptive grouptesting approach, which significantly reduces the number of tests required to identify all positive subjects within a large set of samples. Instead of testing each sample separately, samples are pooled into groups and each pool is tested for SARS-CoV-2 using the standard clinically approved PCRbased diagnostic assay. Each sample is part of multiple pools, using a combinatorial pooling strategy based on compressed sensing designed for maximizing the ability to identify all positive individuals. We evaluated P-BEST using leftover samples that were previously clinically tested for COVID-19. In our current proof-of-concept study we pooled 384 patient samples into 48 pools providing an 8-fold increase in testing efficiency. Five sets of 384 samples, containing 1-5 positive carriers were screened and all positive carriers in each set were correctly identified. P-BEST provides an efficient and easy-to-implement solution for increasing testing capacity that will work with any clinically approved genome-extraction and PCR-based diagnostic methodologies.

Application-oriented mathematical algorithms for group testing

“…We start with pools of size 3. If a result is positive with samples (1, 2, 3), then in the next round, we take an unexamined sample 4 and we test the pools (1,4) and (2,4).…”

“…If, say (1,4) was positive but (2,4) was negative, then we report that sample 1 is positive, samples 2 and 4 are negative, and we put sample 3 back to the list of unexamined samples.…”

“…Phases 7-8. For 7% < P < 10%, we test the samples 7 by 7 (for 8% < P < 10%) or 8 by 8 (for 7% < P < 9%), and if positive, then we test two pools of 3 samples: (1, 2, 3) and (1,4,5). If both are negative, then we use the algorithm in Phase 2 or 3 for the other 2 or 3 samples.…”

“…If one of them is positive, then finish with the algorithm in Phase 2 with the two suspicious samples. If both (1,2,3) and (1,4,5) are positive, then we test the pool (1,2), and if negative, then sample 3 must be positive and the others should put back to the list of unexamined samples, or if (2, 3) is positive, then we apply the algorithm in Phase 2 for samples 2 and 3, and Phase 3 for samples 1, 4, 5.…”

“…Very recently, a short medical paper was published about an appicable and applied solution for a non-adaptive case (without showing the exact algorithm) [4]. This also shows the urgent need of a bridge between combinatorics and application.…”