Optimization of group size in pool testing strategy for SARS‐CoV‐2: A simple mathematical model

• Published in: Journal of medical virology Vol. 92; no. 10; pp. 1988 - 1994
• Main Authors: Aragón‐Caqueo, Diego, Fernández‐Salinas, Javier, Laroze, David
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.10.2020; John Wiley and Sons Inc
• Subjects: coronavirus; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 - epidemiology; COVID-19 Nucleic Acid Testing - methods; Group size; Group theory; Humans; Mass Screening - methods; Mathematical analysis; Mathematical models; modeling; Models, Theoretical; Optimization; Pandemics; Polymerase chain reaction; pool testing; Prevalence; Public health; Reverse Transcriptase Polymerase Chain Reaction; RNA-directed DNA polymerase; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Strategy; Subgroups; Viral diseases; Virology; coronavirus; pool testing; modeling; strategy; public health
• ISSN: 0146-6615; 1096-9071; 1096-9071
• DOI: 10.1002/jmv.25929

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.