Author: Haran Shani-Narkiss; Omri David Gilday; Nadav Yayon; Itamar Daniel Landau
Title: Efficient and Practical Sample Pooling High-Throughput PCR Diagnosis of COVID-19 Document date: 2020_4_7
ID: 6ji8dkkz_42
Snippet: First of all, even if the underlying probability of positive samples is known, the actual number of positive samples in a total of N samples will have a standard deviation that scales as 1 √ ℠. This intrinsic uncertainty will be significant for small N. To check robustness to intrinsic uncertainty in the actual frequency of positive samples, we simulate both algorithms for N=128. We find that this introduces a standard deviation of about 10 .....
Document: First of all, even if the underlying probability of positive samples is known, the actual number of positive samples in a total of N samples will have a standard deviation that scales as 1 √ ℠. This intrinsic uncertainty will be significant for small N. To check robustness to intrinsic uncertainty in the actual frequency of positive samples, we simulate both algorithms for N=128. We find that this introduces a standard deviation of about 10 tests, and that this variability is in practice roughly independent of p and comparable between both methods, though slightly lower in the method of one-time pooling (Fig 2) .
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