An optimal design for hierarchical generalized group testing

TL;DR

This paper introduces a dynamic programming approach to design optimal hierarchical group testing strategies, significantly improving efficiency over previous methods for large populations in disease screening.

Contribution

It develops a novel dynamic programming algorithm to find optimal group testing strategies, outperforming prior approximation methods in efficiency and applicability.

Findings

The new algorithm is more efficient than previous approaches.

Optimal strategies minimize the expected number of tests.

The method is effective even with imperfect tests.

Abstract

Choosing an optimal strategy for hierarchical group testing is an important problem for practitioners who are interested in disease screening with limited resources. For example, when screening for infectious diseases in large populations, it is important to use algorithms that minimize the cost of potentially expensive assays. Black et al. (2015) described this as an intractable problem unless the number of individuals to screen is small. They proposed an approximation to an optimal strategy that is difficult to implement for large population sizes. In this article, we develop an optimal design with respect to the expected total number of tests that can be obtained using a novel dynamic programming algorithm. We show that this algorithm is substantially more efficient than the approach proposed by Black et al. (2015). In addition, we compare the two designs for imperfect tests. R code is provided for the practitioner.