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 serious and rapid improvement to this situation . In this note, we compare several well-established group testing schemes in the context of qPCR testing for COVID-19. We include example calculations, where we indicate which testing architectures yield the greatest efficiency gains in various settings. We find that for identification of individuals with COVID-19, array testing is usually the best choice  , while for estimation of COVID-19 prevalence rates in the total population, Gibbs-Gower testing usually provides the most accurate estimates given a fixed and relatively small number of tests  . This note is intended as a helpful handbook for labs implementing group testing methods.
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.
R
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serious and rapid improvement to this situation
substantial and inexpensive expansion of testing capacity
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well-established
popular
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include example calculations, where we indicate which testing architectures yield the greatest efficiency gains in various settings. We find that
find that in practical settings,
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array testing is usually
Dorfman testing is
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at prevalences up to 30\%
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usually provides the most accurate estimates
is the best choice at prevalences up to 30\%
A
. 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