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

Noam Shental; Shlomia Levy; Shosh Skorniakov; Vered Wuvshet; Yonat Shemer-Avni; Angel Porgador; Tomer Hertz

To test the robustness of P-BEST we considered two types of potential noise factors. First, variation in initial RNA levels may cause samples to 'disappear' from all or part of the pools. Variation in RNA levels were estimated from Qubit measurements of 48 samples. The average RNA concentration was 15ng/µl, with standard deviation of 7ng/µl (Supplemental Figure 1B) . These values were used in our simulations. A second possible source of noise is due to PCR amplification, which may fail in a certain number of pools. Supplemental To assess the effects of variations in RNA levels, we measured the average number of false positive and false negative detections as a function of the true number of carriers across 3000 simulations in two scenarios: (1) No noise in RNA levels (black square) and (2) RNA noise based on the measured variation of RNA levels across 48 samples (see Supplemental Figure 1B ). The false positive (left panel) and false negative (right panel) detection rates for the two scenarios show that RNA variation does not significantly degrade P-BEST performance. All simulations considered one dropped pool.