  With the increasing spread of COVID-19, it is important to systematically
test more and more people. The current strategy for test-kit allocation is
mostly rule-based, focusing on individuals having (a) symptoms for COVID-19,
(b) travel history or (c) contact history with confirmed COVID-19 patients.
Such testing strategy may miss out on detecting asymptomatic individuals who
got infected via community spread. Thus, it is important to allocate a separate
budget of test-kits per day targeted towards preventing community spread and
detecting new cases early on.
  In this report, we consider the problem of allocating test-kits and discuss
some solution approaches. We believe that these approaches will be useful to
contain community spread and detect new cases early on. Additionally, these
approaches would help in collecting unbiased data which can then be used to
improve the accuracy of machine learning models trained to predict COVID-19
infections.
