Artificial intelligence for climate–health early warning systems in the Horn of Africa: opportunities, challenges, and a roadmap for action

Abstract

Climate extremes, conflict, and population displacement converge in the Horn of Africa to accelerate outbreaks of climate-sensitive infectious diseases, whereas existing health surveillance systems remain fragmented and largely reactive. This Perspective examines the potential of artificial intelligence (AI) to strengthen climate–health early warning by integrating satellite earth observations, routine disease surveillance, and mobility-based vulnerability indicators into anticipatory decision support systems. Drawing on global experience and region-specific constraints, we identified critical barriers to implementation, including data fragmentation, infrastructure gaps, workforce shortages, governance silos, and unresolved ethical risks. We propose a five-layer conceptual framework for an AI-enabled Climate–Health Early Warning System (CHEWS) tailored to fragile and conflict-affected settings, alongside a phased regional policy roadmap anchored within the Intergovernmental Authority on Development (IGAD). Emphasizing data sovereignty, participatory governance, and privacy-by-design, this study positions AI-CHEWS as a feasible pathway for shifting the region from reactive outbreak responses to anticipatory public health actions that enhance climate resilience and equity. Clinical Trial Number: The authors declare that they have no competing interests.