Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data
This work addresses the challenge of automated contact tracing for rapidly spreading diseases, offering a domain-specific solution that is incremental in combining graph learning with a new centrality metric.
The paper tackled the problem of tracking infectious disease transmission routes using real human mobility data, achieving an F1-score of 94% for identifying transmission events and reducing the effective reproduction rate by 71% with bidirectional tracing when only 30% of symptomatic individuals are tested.
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.