Network Group Testing

TL;DR

This paper introduces a network-based group testing method that leverages social network information to efficiently identify infected individuals, reducing the number of tests needed especially in populations with strong community structures.

Contribution

The paper proposes a novel network grouping approach that outperforms traditional Dorfman testing by utilizing social network data to improve testing efficiency.

Findings

Network grouping reduces tests compared to Dorfman testing.

Strong community structure enhances network grouping performance.

Empirical data from a Danish university supports effectiveness.

Abstract

We consider the problem of identifying infected individuals in a population of size N. We introduce a group testing approach that uses significantly fewer than N tests when infection prevalence is low. The most common approach to group testing, Dorfman testing, groups individuals randomly. However, as communicable diseases spread from individual to individual through underlying social networks, our approach utilizes network information to improve performance. Network grouping, which groups individuals by community, weakly dominates Dorfman testing in terms of the expected number of tests used. Network grouping's outperformance is determined by the strength of community structure in the network. When networks have strong community structure, network grouping achieves the lower bound for two-stage testing procedures. As an empirical example, we consider the scenario of a university testing its population for COVID-19. Using social network data from a Danish university, we demonstrate network grouping requires significantly fewer tests than Dorfman. In contrast to many proposed group testing approaches, network grouping is simple for practitioners to implement. In practice, individuals can be grouped by family unit, social group, or work group.