Near-Optimal Pool Testing under Urgency Constraints

TL;DR

This paper presents near-optimal pool testing algorithms that efficiently identify individual traits with minimal tests, accounting for urgency, and approach theoretical best performance while maintaining practicality.

Contribution

It introduces algorithms that are provably close to optimal in test efficiency and incorporate urgency considerations in sample processing.

Findings

Algorithms guarantee exact results with at most 1/0.99 times the tests of the optimal.

Approaching the theoretical optimal increases complexity, reducing practicality.

Algorithms enable earlier identification of some individuals, facilitating urgent testing.

Abstract

Detection of rare traits or diseases in a large population is challenging. Pool testing allows covering larger swathes of population at a reduced cost, while simplifying logistics. However, testing precision decreases as it becomes unclear which member of a pool made the global test positive. In this paper we discuss testing strategies that provably approach best-possible strategy - optimal in the sense that no other strategy can give exact results with fewer tests. Our algorithms guarantee that they provide a complete and exact result for every individual, without exceeding $1/0.99$ times the number of tests the optimal strategy would require. This threshold is arbitrary: algorithms closer to the optimal bound can be described, however their complexity increases, making them less practical. Moreover, the way the algorithms process input samples leads to some individuals' status to be known sooner, thus allowing to take urgency into account when assigning individuals to tests.