Optimal targeted mass screening in non‐uniform populations with multiple tests and schemes
研究如何在预算有限下，针对非均匀人群设计最优的大规模筛查方案，通过多维度分数背包问题求解，并用美国新冠筛查数据验证，发现新策略显著降低误分类率。
Abstract We study the problem of designing optimal targeted mass screening of non‐uniform populations. Mass screening is an essential tool that is widely utilized in a variety of settings, for example, preventing infertility through screening programs for sexually transmitted diseases, ensuring a safe blood supply for transfusion, and mitigating the transmission of infectious diseases. The objective of mass screening is to maximize the overall classification accuracy under limited budget. In this paper, we address this problem by proposing a proactive optimization‐based framework that factors in population heterogeneity, limited budget, different testing schemes, the availability of multiple assays, and imperfect assays. By analyzing the resulting optimization problem, we take advantage of the structure of the problem as a multi‐dimensional fractional knapsack problem and identify an efficient globally convergent threshold‐style solution scheme that fully characterizes an optimal solution across the entire budget spectrum. Using real‐world data, we conduct a geographic‐based nationwide case study on targeted COVID‐19 screening in the United States. Our results reveal that the identified screening strategies substantially outperform conventional practices by significantly lowering misclassifications while utilizing the same amount of budget. Moreover, our results provide valuable managerial insights with regard to the distribution of testing schemes, assays, and budget across different geographic regions.