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We provide a comparison of general strategies for group testing in view of their application to medical diagnosis in the current COVID-19 pandemic. We find significant efficiency gaps between different group testing strategies in realistic scenarios for SARS-CoV-2 testing, highlighting the need for an informed decision of the pooling protocol depending on estimated prevalence, target specificity, and high- vs. low-risk population. For example, using one of the presented methods, all 1.47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if an infection rate up to 0.4% is assumed. Using 1 million tests, the 6.69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. Altogether this work may help provide a basis for efficient upscaling of current testing procedures, fine grained towards the desired study population, e.g. cross-sectional versus health-care workers and adapted mixtures thereof. For comparative visualization and querying of the precomputed results we provide an interactive web application. The source code for computation is open and freely available.
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Verdun et al. (2020) studied this question.
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