Rapid, large-scale, and effective detection of COVID-19 via non-adaptive testing
Matthias Täufer
doi: https://doi.org/10.1101/2020.04.06.028431
Abstract
Pooling of samples can increase lab capacity when using Polymerase chain reaction (PCR) to detect infections such as COVID-19. However, pool testing is typically performed via an adaptive testing strategy which requires a feedback loop in the lab and at least two PCR runs to confirm positive results. This can cost precious time. We discuss a non-adaptive testing method where each sample is distributed in a prescribed manner over several pools, and which yields reliable results after one round of testing. More precisely, assuming knowledge about the overall infection incidence rate, we calculate explicit error bounds on the number of false positives which scale very favourably with pool size and sample multiplicity.
*注，本文为预印本论文手稿，是未经同行评审的初步报告，其观点仅供科研同行交流，并不是结论性内容，请使用者谨慎使用.
