AI-Driven One Health Surveillance for Zoonotic Risk Mitigation: Insights from TBE and CCHF

Emerging vector-borne zoonoses are driven by climate change, biodiversity loss and land-use change. Surveillance remains fragmented, largely reactive and often blind to the risk signals that precede spillover. This narrative review examines how artificial intelligence (AI) and machine learning (ML), integrated with georeferenced and climatic data, could support predictive One Health surveillance. Using tick-borne encephalitis (TBE) and Crimean-Congo hemorrhagic fever (CCHF) as case studies, we distinguish evidence from conventional statistical models, ML models and deep-learning applications. ML models combining climatic, land-cover and host variables have shown promising retrospective performance in mapping tick habitat and TBE and CCHF risk, whereas deep learning has been applied mainly to tick identification. However, AI evidence specific to TBE and CCHF remains limited: few disease-specific studies exist, validation is largely internal, no model has been evaluated prospectively and some reported figures derive from synthetic data. The case for AI-driven One Health surveillance of these diseases therefore rests more on methodological feasibility than on disease-specific prospective evidence. We also discuss validation practices, data quality and the EU AI Act. AI may enable a shift from costly emergency response toward validated Early Warning Systems if methodological, infrastructural and regulatory gaps are addressed. We outline priorities for researchers, veterinarians, clinicians and policymakers.