Positively Correlated Samples Save Pooled Testing Costs

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.

14 pages, 8 figures, submitted for publication

References in corpus (5)

• Cooperative Game Theory Approaches for Network Partitioning
• A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons
• Graph Evolution: Densification and Shrinking Diameters
• A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
• Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation

Cited by in corpus (5)

• An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic
• AC-DC: Amplification Curve Diagnostics for Covid-19 Group Testing
• Group Testing with Side Information via Generalized Approximate Message Passing
• Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors
• Contact Tracing Information Improves the Performance of Group Testing Algorithms