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Testing for active SARS-CoV-2 infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies in the presence of limited resource and/or multiple competing test options. In this paper, we provide a mathematical framework for defining an optimal strategy for allocating viral tests. The framework accounts for imperfect test results, testing in certain high-risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the goal of testing. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We extend our proposed method to address the challenge of allocating two different types of tests with different cost and accuracy (for example the expensive but more accurate RT-PCR test versus the cheap but less accurate rapid antigen test), administered under budget constraints. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision-making at a community level, adapted to different stages of the pandemic. We use distribution of tests in New York City during the initial wave of the COVID-19 outbreak as a sample example. We also show how this framework can be useful to reopening of college campuses.
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?:doi
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10.1101/2020.12.09.20246629
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document_parses/pdf_json/5d858ceefedefdf0946f0580f51592e7dfec5813.json
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Optimal test allocation strategy for COVID-19
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