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Free keywords:
Statistics, Applications, stat.AP,Computer Science, Learning, cs.LG,Quantitative Biology, Populations and Evolution, q-bio.PE
Abstract:
Testing is recommended for all close contacts of confirmed COVID-19 patients.
However, existing group testing methods are oblivious to the circumstances of
contagion provided by contact tracing. Here, we build upon a well-known
semi-adaptive pool testing method, Dorfman's method with imperfect tests, and
derive a simple group testing method based on dynamic programming that is
specifically designed to use the information provided by contact tracing.
Experiments using a variety of reproduction numbers and dispersion levels,
including those estimated in the context of the COVID-19 pandemic, show that
the pools found using our method result in a significantly lower number of
tests than those found using standard Dorfman's method, especially when the
number of contacts of an infected individual is small. Moreover, our results
show that our method can be more beneficial when the secondary infections are
highly overdispersed.
