Abstract
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.
Highlights
The fast global spread of the novel coronavirus is responsible for the current pandemic, known as coronavirus disease 2019 (Covid-19)
Testing capacities can be increased by a modification of the testing protocol
We denote the number of people in one pool, i.e. the pool size, by M
Summary
The fast global spread of the novel coronavirus is responsible for the current pandemic, known as coronavirus disease 2019 (Covid-19). Diagnostic tests for the novel coronavirus (nCov) are being employed worldwide. Used tests for Covid-19 detect the novel coronavirus RNA genome, using the realtime polymerase chain reaction method. The capacities for such tests, are limited by the used reactants, machine time, lab personnel time and overall logistics. Combining probes from several individuals and testing them together reduces the total amount of tests needed. This method, known as pooling or group testing, has been known for a long time [1].
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