Positively Correlated Samples Save Pooled Testing Costs

TL;DR

This paper demonstrates that accounting for positive correlations among samples, such as within families, can significantly reduce COVID-19 pooled testing costs by optimizing group testing strategies.

Contribution

It provides a rigorous proof that positive correlation allows further cost savings with the Dorfman two-stage method and proposes a hierarchical algorithm leveraging social graphs for improved pooling.

Findings

Cost reduction of 20%-35% using social graph-based pooling.

Positive correlation enhances efficiency of pooled testing.

Hierarchical algorithm outperforms random pooling.

Abstract

The group testing approach that achieves significant cost reduction over the individual testing approach has received a lot of interest lately for massive testing of COVID-19. Many studies simply assume samples mixed in a group are independent. However, this assumption may not be reasonable for a contagious disease like COVID-19. Specifically, people within a family tend to infect each other and thus are likely to be positively correlated. By exploiting positive correlation, we make the following two main contributions. One is to provide a rigorous proof that further cost reduction can be achieved by using the Dorfman two-stage method when samples within a group are positively correlated. The other is to propose a hierarchical agglomerative algorithm for pooled testing with a social graph, where an edge in the social graph connects frequent social contacts between two persons. Such an algorithm leads to notable cost reduction (roughly 20%-35%) compared to random pooling when the Dorfman two-stage algorithm is applied.