A comparison of group testing architectures for COVID-19 testing

Abstract

An important component of every country's COVID-19 response is fast and efficient testing - to identify and isolate cases, as well as for early detection of local hotspots. For many countries, producing a sufficient number of tests has been a serious limiting factor in their efforts to control COVID-19 infections. Group testing is a well-established mathematical tool, which can provide a substantial and inexpensive expansion of testing capacity. In this note, we compare several popular group testing schemes in the context of qPCR testing for COVID-19. We find that in practical settings, for identification of individuals with COVID-19, Dorfman testing is the best choice at prevalences up to 30%, while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing is the best choice at prevalences up to 30% given a fixed and relatively small number of tests. For instance, at a prevalence of up to 2%, Dorfman testing gives an efficiency gain of 3.5--8; at 1% prevalence, Gibbs-Gower testing gives an efficiency gain of 18, even when capping the pool size at a feasible number . This note is intended as a helpful handbook for labs implementing group testing methods.