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

arXiv: https://arxiv.org/abs/2501.18531 · HTML: https://mlsanity.com/paper/2501.18531/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Class-Imbalanced Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.04300/) (2023)
- [Halting SARS-CoV-2 by Targeting High-Contact Individuals](https://mlsanity.com/paper/2005.08907/) (2020)
- [Targeted Pandemic Containment Through Identifying Local Contact Network Bottlenecks](https://mlsanity.com/paper/2006.06939/) (2020)

## Abstract (arXiv)

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.