Ned Augenblick, Ziad Obermeyer and Ao Wang

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Group testing increases efficiency by pooling individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. Optimal grouping strategy is well studied for oneoff testing scenarios, in populations with no correlations in risk and reasonably well-known prevalence rates. We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection transmission, and highly uncertain risk. First, repeated testing mechanically lowers prevalence at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around √ x. However, this calculation omits a further benefit of frequent testing: removing infections from the population lowers intra-group transmission, which lowers prevalence and generates further efficiency. For this reason, increasing frequency can paradoxically reduce total testing cost. Second, we show that group size and efficiency increases with intra-group risk correlation, which is expected given spread within natural groupings (e.g., in workplaces, classrooms, etc). Third, because optimal groupings depend on disease prevalence and correlation, we show that better risk predictions from machine learning tools can drive large efficiency gains. We conclude that frequent group testing, aided by machine learning, is a promising and inexpensive surveillance strategy.