  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.
