Dynamic Welfare-Maximizing Pooled Testing

2026-01-30

AI Summary

This study addresses the challenge of maximizing social welfare—defined as the total utility of correctly identified healthy individuals—in resource-constrained public health screening through dynamic pooling strategies. Departing from conventional static approaches or those solely minimizing test counts, this work is the first to integrate dynamic optimization with explicit social welfare objectives, systematically investigating sequential and adaptive allocation of testing resources. The authors develop and evaluate several algorithms, including exact dynamic programming, greedy heuristics, mixed-integer programming relaxations, and learning-based policies. Experimental results demonstrate that, under tight testing budgets, dynamic strategies substantially outperform static baselines. Among them, the greedy heuristic achieves a favorable trade-off between performance and computational efficiency, while learning-based methods do not consistently surpass heuristic approaches.

Technology Category

• Search and Optimization: Heuristic Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Pooled testing is a common strategy for public health disease screening under limited testing resources, allowing multiple biological samples to be tested together with the resources of a single test, at the cost of reduced individual resolution. While dynamic and adaptive strategies have been extensively studied in the classical pooled testing literature, where the goal is to minimize the number of tests required for full diagnosis of a given population, much of the existing work on welfare-maximizing pooled testing adopts static formulations in which all tests are assigned in advance. In this paper, we study dynamic welfare-maximizing pooled testing strategies in which a limited number of tests are performed sequentially to maximize social welfare, defined as the aggregate utility of individuals who are confirmed to be healthy. We formally define the dynamic problem and study algorithmic approaches for sequential test assignment. Because exact dynamic optimization is computationally infeasible beyond small instances, we evaluate a range of strategies (including exact optimization baselines, greedy heuristics, mixed-integer programming relaxations, and learning-based policies) and empirically characterize their performance and tradeoffs using synthetic experiments. Our results show that dynamic testing can yield substantial welfare improvements over static baselines in low-budget regimes. We find that much of the benefit of dynamic testing is captured by simple greedy policies, which substantially outperform static approaches while remaining computationally efficient. Learning-based methods are included as flexible baselines, but in our experiments they do not reliably improve upon these heuristics. Overall, this work provides a principled computational perspective on dynamic pooled testing and clarifies when dynamic assignment meaningfully improves welfare in public health screening.

Problem

• pooled testing
• dynamic optimization
• social welfare
• sequential testing
• resource allocation

Innovation

• dynamic pooled testing
• welfare maximization
• sequential testing
• greedy heuristics
• public health screening