TransmiRisk-AI: an agentic AI-based risk assessment framework for zoonotic diseases using integrated surveillance data

Introduction Zoonotic disease outbreaks are increasing in frequency and complexity, highlighting the need for intelligent surveillance systems capable of real-time monitoring and risk assessment. Existing surveillance approaches are often reactive, fragmented, and dependent on manual analysis, limiting timely public health responses. Methods This study presents TransmiRisk-AI, an agentic AI-driven surveillance framework designed to support automated zoonotic disease risk monitoring and decision-making. The framework integrates a hybrid risk scoring model with a multi-agent architecture comprising data ingestion, risk assessment, alert generation, execution, logging, and orchestration agents. These agents collaboratively automate data collection, preprocessing, risk evaluation, outbreak monitoring, and alert dissemination. To enhance situational awareness, the system incorporates outbreak intelligence from WHO Disease Outbreak News and dynamically updates disease risk profiles based on outbreak recency, frequency, and severity. An interactive Streamlit dashboard provides transparent visualization of risk categories, outbreak trends, and system activities. Results Case studies involving the Nipah virus and Crimean-Congo hemorrhagic fever demonstrate the framework's ability to adapt to evolving epidemiological conditions. Discussion TransmiRisk-AI offers a scalable and interpretable digital health solution that supports proactive surveillance and timely decision-making within a One Health context.