Group testing for overlapping communities

TL;DR

This paper introduces algorithms that utilize known community structures within populations to enhance the efficiency of group testing for identifying infected individuals, significantly reducing the number of tests required.

Contribution

It presents novel algorithms that incorporate community information into group testing, improving efficiency over traditional methods.

Findings

Community-aware algorithms reduce testing resources needed.

Significant efficiency gains in adaptive and non-adaptive testing.

Applicable to populations with overlapping community memberships.

Abstract

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in connected communities: each individual participates in one or more communities, and the infection probability of each individual depends on the communities (s)he participates in. Use cases include students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that making testing algorithms aware of the community structure, can significantly reduce the number of tests needed both for adaptive and non-adaptive group testing.