A BEACON for Novel Disease Threats: Leveraging Artificial Intelligence for Informal Event-Based Outbreak Surveillance

Comprehensive Summary

This prospective, multicenter study asked whether integrating artificial intelligence and large language models into event-based surveillance systems could improve the detection and analysis of emerging infectious disease outbreaks. Researchers developed the Biothreats Emergence, Analysis and Communications Network (BEACON), an event-based surveillance platform that uses a domain-adapted PandemIQ Llama large language model to perform natural-language processing tasks such as information extraction, source credibility assessment, and preliminary risk evaluation. The platform analyzed unstructured outbreak signals, including news reports, user submissions, and global monitoring feeds, from partner systems such as HealthMap, with expert analysts verifying and contextualizing results. Between April and November 2025, the system generated approximately n=1,300 disease reports describing nearly 600 outbreaks across more than 100 diseases in 195 countries and territories, with active users in 168 countries. Text data were processed using LLM-based agents trained on medical literature, historical outbreak data, and expert feedback to extract epidemiologic details, evaluate urgency and credibility, and draft preliminary surveillance summaries for editorial review. Because the article describes the operational deployment of the platform rather than a traditional diagnostic accuracy study, the evaluation focused on reporting scale and workflow performance rather than metrics such as AUROC or F1 scores. The analysis showed that the system successfully identified and reported hundreds of global disease events, demonstrating the ability of LLM-supported surveillance to rapidly synthesize large volumes of unstructured information. The structured database generated by BEACON categorized reports by disease, geography, pathogen, and symptoms, enabling large-scale analysis of trends across time and regions. Secondary analyses highlighted the potential for future predictive modeling by extracting epidemiologic features and text embeddings from outbreak reports to train downstream machine-learning models. Additional observations emphasized that the LLM improved efficiency in tasks such as translation, text synthesis, and signal triage, allowing human analysts to focus on contextual interpretation and verification. Limitations include reliance on informal online data sources that may introduce selection bias, possible inaccuracies in automated text interpretation, and the need for human oversight to resolve conflicting or incomplete information. External validation of predictive models was not performed, and subgroup or fairness analyses were not reported. Findings, therefore, reflect improvements in surveillance workflow and information synthesis rather than direct evidence of improved outbreak outcomes or clinical impact.

Outcomes and Implications

The study suggests that domain-adapted large language models integrated into event-based surveillance platforms can accelerate the identification and contextualization of potential disease outbreaks while maintaining human oversight. In practice, systems like BEACON could support earlier situational awareness for public health agencies by rapidly aggregating signals from global information streams and presenting structured summaries for epidemiologists and policymakers. However, translation to bedside clinical care remains indirect and will require validation studies demonstrating that earlier detection improves outbreak response, patient outcomes, or health system preparedness. Future development could integrate additional data sources, such as genomic surveillance, wastewater monitoring, and environmental data, to enhance predictive capabilities and strengthen global infectious-disease intelligence.