Selected article for: "batch size and initial batch size"
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_20
    Snippet: The copyright holder for this preprint . https://doi.org/10.1101/2020.04.06.20052159 doi: medRxiv preprint Our method is as follows: First, given the expected frequency of positive samples, p, calculate the initial batch-size, b, that yields as close to 50% positive rate. In all subsequent rounds of testing, simply divide any positive batches in half and repeat, essentially performing a binary tree search algorithm. The probability that an entire.....
    Document: The copyright holder for this preprint . https://doi.org/10.1101/2020.04.06.20052159 doi: medRxiv preprint Our method is as follows: First, given the expected frequency of positive samples, p, calculate the initial batch-size, b, that yields as close to 50% positive rate. In all subsequent rounds of testing, simply divide any positive batches in half and repeat, essentially performing a binary tree search algorithm. The probability that an entire batch is negative is the product of the probabilities that each sample is negative. Thus, given p, the probability that an entire batch of size b is negative is (1 − ) , and we want to choose b so that this number equals 0.5. For example, suppose the frequency of positive samples is p=0.02, i.e. 1 in 50 samples is positive. It turns out that a batch of 34 samples has approximately a 50-50 chance of being entirely negative or having at least one positive, i.e.

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