Optimization of group size in pool testing strategy for SARS‐CoV‐2: A simple mathematical model - Aragón‐Caqueo, Diego; Fernández‐Salinas, Javier; Laroze, David (Journal of medical virology, 2020)
Coronavirus disease (Covid‐19) has reached unprecedented pandemic levels and is affecting almost every country in the world. Ramping up the testing capacity of a country supposes an essential public health response to this new outbreak. A pool testing strategy where multiple samples are tested in a single reverse transcriptase‐polymerase chain reaction (RT‐PCR) kit could potentially increase a country's testing capacity. The aim of this study is to propose a simple mathematical model to estimate the optimum number of pooled samples according to the relative prevalence of positive tests in a particular healthcare context, assuming that if a group tests negative, no further testing is done whereas if a group tests positive, all the subjects of the group are retested individually. The model predicts group sizes that range from 11 to 3 subjects. For a prevalence of 10% of positive tests, 40.6% of tests can be saved using testing groups of four subjects. For a 20% prevalence, 17.9% of tests can be saved using groups of three subjects. For higher prevalences, the strategy flattens and loses effectiveness. Pool testing individuals for severe acute respiratory syndrome coronavirus 2 is a valuable strategy that could considerably boost a country's testing capacity. However, further studies are needed to address how large these groups can be, without losing sensitivity on the RT‐PCR. The strategy best works in settings with a low prevalence of positive tests. It is best implemented in subgroups with low clinical suspicion. The model can be adapted to specific prevalences, generating a tailored to the context implementation of the pool testing strategy. Highlights ‐Increasing testing capacity of a country is a key Public Health strategy in the pandemic. ‐A pool testing strategy could potentially increase a country's testing capacity, especially when implemented in lower clinical suspicion groups. ‐We provide a mathematical model to estimate the optimum number of subjects to include in a pool test, based on historical prevalences of positive results.