@article{bece778c696147a6a9987334a02494df,

title = "Tests in short supply? Try group testing",

abstract = "Christopher R. Bilder, Peter C. Iwen, Baha Abdalhamid, Joshua M. Tebbs and Christopher S. McMahan explain how, by pooling specimens, testing capacity for SARS-CoV-2 can be increased.",

author = "Bilder, {Christopher R.} and Iwen, {Peter C.} and Baha Abdalhamid and Tebbs, {Joshua M.} and McMahan, {Christopher S.}",

note = "Funding Information: This work was supported by Grant R01 AI121351 from the National Institutes of Health. Funding Information: This work was supported by Grant R01 AI121351 from the National Institutes of Health. Define T as a random variable for the number of tests from a group of size s, and define G as a binary random variable indicating whether a group tests positive (1) or negative (0). The expected number of tests for a group using Dorfman testing is With perfect test accuracy, P(G = 1) = 1 – (1 – p)s, where p is the disease prevalence. The most efficient group size is the one that minimises E(T)/s, the expected number of tests per individual for the group. Finucan7 showed that this minimum can be approximated by choosing the group size as the next integer larger than 1/√p, but one can also iterate over a range of group sizes to find the solution.8 In application, p is unknown, so an estimate is substituted. Two measures of efficiency for group testing relative to testing specimens separately are the percentage reduction in the expected number of tests, 100(1 – E(T)/s)%; and the expected increase in the testing capacity, 100{1/[E(T)/s] – 1}%. Publisher Copyright: {\textcopyright} 2020 The Royal Statistical Society",

year = "2020",

month = jun,

day = "1",

doi = "10.1111/1740-9713.01399",

language = "English (US)",

volume = "17",

pages = "15--16",

journal = "Significance",

issn = "1740-9705",

publisher = "Wiley-Blackwell",

number = "3",

}