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The effectiveness of digital contact tracing during extended outbreaks of airborne infectious diseases, such as COVID-19, influenza, or RSV, can be hindered by limited social compliance and delays in real-world testing. Prior work has shown the utility of graph learning for bidirectional contact tracing and multi-agent reinforcement learning (MARL) for disease mitigation; however, they rely on post-hoc analysis and full testing compliance, thus limiting real-time applicability. To address these limitations, we propose a new framework for online automated bidirectional contact tracing and disease-aware navigation. Our framework iteratively identifies infectious culprits, infers individual health statuses, and deploys agents to minimize infectious exposure without requiring Oracle health information. Our proposed framework achieves an average online backwards tracing F1-score of 92% and estimates the total case counts within 5% accuracy, even under conditions of probabilistic testing with significant social hesitancy. Additionally, our proposed agent-based navigation system can reduce the disease spread by 29%. These results demonstrate the framework’s potential to address critical gaps in traditional disease surveillance and mitigation models and improve real-time public health interventions.
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This content will become publicly available on January 1, 2026
Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data
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.
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- Award ID(s):
- 2107085
- PAR ID:
- 10639031
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- Page Range / eLocation ID:
- 205 to 222
- Subject(s) / Keyword(s):
- Graph neural networks Infection dynamics COVID-19
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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