Diagnostic testing for the novel coronavirus is an important tool to fight the coronavirus disease (Covid-19) pandemic. However, testing capacities are limited. A modified testing protocol, whereby a number of probes are ‘pooled’ (i.e. grouped), is known to increase the capacity for testing. Here, we model pooled testing with a double-average model, which we think to be close to reality for Covid-19 testing. The optimal pool size and the effect of test errors are considered. The results show that the best pool size is three to five, under reasonable assumptions. Pool testing even reduces the number of false positives in the absence of dilution effects.
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【저자키워드】 COVID-19, coronavirus, Epidemiology, Laboratory tests, Mathematical modelling, 【초록키워드】 coronavirus disease, pandemic, protocol, Novel coronavirus, diagnostic testing, False positive, Dilution, pool, probe, Effects, FIVE, absence, reduce, 【제목키워드】 Prevalence, pooling,
【저자키워드】 COVID-19, coronavirus, Epidemiology, Laboratory tests, Mathematical modelling, 【초록키워드】 coronavirus disease, pandemic, protocol, Novel coronavirus, diagnostic testing, False positive, Dilution, pool, probe, Effects, FIVE, absence, reduce, 【제목키워드】 Prevalence, pooling,