Contact Tracing Information Improves the Performance of Group Testing Algorithms

TL;DR

This paper demonstrates that integrating contact tracing information into group testing algorithms enhances their ability to accurately identify infected individuals, optimizing resource use during pandemics.

Contribution

It introduces methods to incorporate contact tracing side information into group testing algorithms and matrix design, improving detection sensitivity and specificity.

Findings

Contact tracing data improves group testing accuracy.

Incorporating SI into algorithms enhances sensitivity and specificity.

Designing pooling matrices with SI offers limited additional gains.

Abstract

Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In group testing, we are given $n$ samples, one per individual. These samples are arranged into $m < n$ pooled samples, where each pool is obtained by mixing a subset of the $n$ individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we use side information (SI) collected from contact tracing (CT) within nonadaptive/single-stage group testing algorithms. We generate CT SI data by incorporating characteristics of disease spread between individuals. These data are fed into two signal and measurement models for group testing, and numerical results show that our algorithms provide improved sensitivity and specificity. We also show how to incorporate CT SI into the design of the pooling matrix. That said, our numerical results suggest that the utilization of SI in the pooling matrix design based on the minimization of a weighted coherence measure does not yield significant performance gains beyond the incorporation of SI in the group testing algorithm.