Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data

TL;DR

This paper introduces a graph learning approach using real human mobility data to improve bidirectional disease contact tracing, significantly reducing disease spread by identifying transmission routes more effectively.

Contribution

It presents a new Infectious Path Centrality metric and demonstrates the effectiveness of bidirectional contact tracing over traditional methods in controlling outbreaks.

Findings

F1-score of 94% for transmission event classification

Bidirectional tracing reduces effective reproduction rate by 71% with 30% testing

Outperforms traditional forward tracing in outbreak control

Abstract

For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification applications provide alerts on potential exposures, a fully automated system is needed to track the infectious transmission routes. To this end, our research leverages large-scale contact networks from real human mobility data to identify the path of transmission. More precisely, we introduce a new Infectious Path Centrality network metric that informs a graph learning edge classifier to identify important transmission events, achieving an F1-score of 94%. Additionally, we explore bidirectional contact tracing, which quarantines individuals both retroactively and proactively, and compare its effectiveness against traditional forward tracing, which only isolates individuals after testing positive. Our results indicate that when only 30% of symptomatic individuals are tested, bidirectional tracing can reduce infectious effective reproduction rate by 71%, thus significantly controlling the outbreak.