Table 1 Summary of the minimum number of test sites needed for each allocation method to outperform proposed multi-objective adaptive allocation as measured by the sum of the evaluation metrics. Column \(\Vert {a} \Vert\) indicates the batch size or number of new test sites added at each time index. We fixed the One-Shot design to 100 sites and terminated the proposed design once it allocated more than 50% of that capacity. For the other adaptive designs, column \(N\) shows how many test sites are needed to either exceed the proposed design’s total score \(\sum _i f_i\) or, if that threshold cannot be reached, to attain the highest achievable total score below it. Column \(f_i\) is the individual score for the component. \(f_1\) is the access score, \(f_2\) is the precision score, and \(f_3\) is the equity score. Note that for each score, higher is better.All listed methods select test sites adaptively, except for the “One-Shot (non-adaptive)” approach. The “Random” method is adaptive in the sense that it selects new test sites at each time point, but unlike the other adaptive methods–which use criteria such as access, precision, equity, or a combination thereof–it selects locations entirely at random.

From: Balancing access, precision, and equity in adaptive test site allocation with an application to COVID-19 in Atlanta, Georgia