COVID-19: Strategies for Allocation of Test Kits
This work tackles the challenge of containing COVID-19 spread by optimizing test allocation, but it appears incremental as it discusses existing approaches without presenting new results.
The paper addresses the problem of allocating COVID-19 test kits to detect asymptomatic cases from community spread, proposing solution approaches aimed at early detection and unbiased data collection for improving machine learning models.
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.