Health Status Discovery for Online Bidirectional Contact Tracing and Disease Aware Navigation

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 backwardstracing 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 agentbased 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.