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.