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Optimization of group size in pool testing strategy for SARS‐CoV‐2: A simple mathematical model
Author(s) -
AragónCaqueo Diego,
FernándezSalinas Javier,
Laroze David
Publication year - 2020
Publication title -
journal of medical virology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 121
eISSN - 1096-9071
pISSN - 0146-6615
DOI - 10.1002/jmv.25929
Subject(s) - group testing , test strategy , context (archaeology) , outbreak , medicine , pandemic , diagnostic test , covid-19 , statistics , virology , disease , mathematics , veterinary medicine , biology , infectious disease (medical specialty) , computer science , software , combinatorics , paleontology , programming language
Abstract 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.