key: cord-0863409-q2bu7o4i
authors: Bilder, Christopher R; Iwen, Peter C; Abdalhamid, Baha
title: Pool size selection when testing for SARS-CoV-2
date: 2020-06-16
journal: Clin Infect Dis
DOI: 10.1093/cid/ciaa774
sha: 47b47da4b1468d968948d4ea997053a9dd4d442a
doc_id: 863409
cord_uid: q2bu7o4i

nan

M a n u s c r i p t Dear Editor-Pooling samples has been proposed in multiple articles as an efficient way to test for SARS-CoV-2 [1] [2] [3] [4] . In particular, Yelin et al [1] showed that SARS-CoV-2 can be detected in pools with up to 32 samples and potentially in pools of 64 samples. They concluded that "this pooling method can be applied immediately in current clinical testing laboratories." However, this research [1] and similar research of others [2] [3] missed answering a very important question: How does one choose the most efficient pool size relative to SARS-CoV-2 prevalence in samples? Without answering this question, laboratories cannot fully benefit from pooling. Our correspondence provides the answer so that laboratories can increase their testing capacity to its fullest potential.

The efficiencies from pooling samples occur when pools test negative. In general, the probability of a negative pool ( ) is given by = (1 -) for a prevalence ( ) and pool size ( ) [5] . For example, the most efficient pool size is four samples when prevalence is 10% (calculation to be discussed shortly). This will lead to 66% of the pools testing negative on average, resulting in three tests saved for each negative pool. On the other hand, choosing a pool size too large can be very inefficient. By changing the size to 32 samples in our example, only 3% of the pools will test negative. We subsequently show that there are no benefits from using this pool size with this prevalence. Similar inefficiencies occur as well when selecting pool sizes that are too small. Yelin et al [1] identified a range of pool sizes that appear to not compromise testing sensitivity.

From this range, one needs to determine the optimal pool size to perform testing most efficiently.

Statistical research has shown in general that this is the pool size that minimizes the average number of tests on a per capita basis ( ) when testing a continuous series of samples, where is A c c e p t e d M a n u s c r i p t a mathematical function of prevalence [5] [6] [7] . Separate testing of each sample corresponds to = 1, and pooling is more efficient when < 1. Expressions for are available [5] [6] [7] , and the optimal pool size can be approximated by the next integer larger 1/√ [8] or found exactly [9] [10] . 

The authors report no conflicts of interest. M a n u s c r i p t 

Evaluation of COVID-19 RT-qPCR test in multisample pools

Pooling of samples for testing for SARS-CoV-2 in asymptomatic people

Sample pooling as a strategy to detect community transmission of SARS-CoV-2

Assessment of specimen pooling to conserve SARS CoV-2 testing resources

Comparison of group testing algorithms for case identification in the presence of test error

The objective function controversy for group testing: Much ado about nothing

Group testing for identification

The blood testing problem

A Shiny app for pooled testing