  We propose two-stage adaptive pooling schemes, 2-STAP and 2-STAMP, for
detecting COVID-19 using real-time reverse transcription quantitative
polymerase chain reaction (RT-qPCR) test kits. Similar to the Tapestry scheme
of Ghosh et al., the proposed schemes leverage soft information from the
RT-qPCR process about the total viral load in the pool. This is in contrast to
conventional group testing schemes where the measurements are Boolean. The
proposed schemes provide higher testing throughput than the popularly used
Dorfman's scheme. They also provide higher testing throughput, sensitivity and
specificity than the state-of-the-art non-adaptive Tapestry scheme. The number
of pipetting operations is lower than state-of-the-art non-adaptive pooling
schemes, and is higher than that for the Dorfman's scheme. The proposed schemes
can work with substantially smaller group sizes than non-adaptive schemes and
are simple to describe. Monte-Carlo simulations using the statistical model in
the work of Ghosh et al. (Tapestry) show that 10 infected people in a
population of size 961 can be identified with 70.86 tests on the average with a
sensitivity of 99.50% and specificity of 99.62. This is 13.5x, 4.24x, and 1.3x
the testing throughput of individual testing, Dorfman's testing, and the
Tapestry scheme, respectively.
