Group testing schemes from codes and designs

TL;DR

This paper introduces a new framework for nonadaptive group testing using $(t, ext{ extepsilon})$-disjunct matrices, connecting coding theory and combinatorial designs to improve identification of defective items with controlled false positives.

Contribution

It establishes a novel link between $(t, ext{ extepsilon})$-disjunct matrices and error correcting codes, providing new estimates and methods for designing group testing schemes.

Findings

Derived bounds for code parameters for group testing

Connected group testing schemes with combinatorial designs

Utilized moments of code distance distributions in analysis

Abstract

In group testing, simple binary-output tests are designed to identify a small number $t$ of defective items that are present in a large population of $N$ items. Each test takes as input a group of items and produces a binary output indicating whether the group is free of the defective items or contains one or more of them. In this paper we study a relaxation of the combinatorial group testing problem. A matrix is called $(t,\epsilon)$-disjunct if it gives rise to a nonadaptive group testing scheme with the property of identifying a uniformly random $t$-set of defective subjects out of a population of size $N$ with false positive probability of an item at most $\epsilon$. We establish a new connection between $(t,\epsilon)$-disjunct matrices and error correcting codes based on the dual distance of the codes and derive estimates of the parameters of codes that give rise to such schemes. Our methods rely on the moments of the distance distribution of codes and inequalities for moments of sums of independent random variables. We also provide a new connection between group testing schemes and combinatorial designs.