Pooled testing and its applications in the COVID-19 pandemic

TL;DR

This paper explores pooled testing strategies for COVID-19, analyzing their mathematical basis, efficiency advantages at low prevalence, and practical implementation issues during pandemics.

Contribution

It provides a comprehensive analysis of pooling strategies, including mathematical models and practical considerations, for effective disease testing during pandemics.

Findings

Pooling strategies are more efficient than individual testing at low disease prevalence.

Two-stage pooling can reduce the number of tests needed significantly.

Practical issues include test accuracy and sample handling complexities.

Abstract

When testing for a disease such as COVID-19, the standard method is individual testing: we take a sample from each individual and test these samples separately. An alternative is pooled testing (or "group testing"), where samples are mixed together in different pools, and those pooled samples are tested. When the prevalence of the disease is low and the accuracy of the test is fairly high, pooled testing strategies can be more efficient than individual testing. In this chapter, we discuss the mathematics of pooled testing and its uses during pandemics, in particular the COVID-19 pandemic. We analyse some one- and two-stage pooling strategies under perfect and imperfect tests, and consider the practical issues in the application of such protocols.