This review demonstrates the effect of climate change on infectious disease emergence, indicating serious implications for health systems.
The relationship between a warming planet and the rise of infectious disease has moved, over the past two decades, from speculation to one of the defining public-health questions of the century. This review synthesises what is now known about how climate change reshapes the emergence, re-emergence, and geographic redistribution of human pathogens. It begins with the conceptual machinery linking climate to disease: the temperature- and moisture-sensitive biology of vectors, pathogens, hosts, and reservoirs, and the way extreme events and gradual warming alter each link in the transmission chain. The review then traces the evidence across the major disease groups, mosquito- and tick-borne infections, water- and food-borne illness, and wildlife-origin zoonoses, and examines the regional patterns and emerging hotspots that modelling has begun to map. Considerable attention is given to the genuine scientific controversy that surrounds attribution, including the non-linear, hump-shaped response of transmission to temperature, the powerful confounding role of poverty, urbanisation, and control programmes, and well-documented cases in which warming has coincided with disease decline rather than rise. From this evidence, three conclusions emerge. First, climate is one driver among several, and its fingerprint is clearest at the cool and hot margins of a pathogen's range. Second, the dominant near-term effect is redistribution, shifting risk poleward and upward into populations and health systems that may be unprepared, rather than a uniform global increase. Third, the breadth of pathways through which climatic hazards aggravate disease is too large for adaptation alone, underscoring the need to couple resilient surveillance and health systems with reductions in greenhouse-gas emissions. The review closes by setting out practical priorities for early-warning systems, integrated One Health surveillance, and equitable adaptation, alongside the knowledge gaps that most constrain prediction.
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Snekha C., Mervin Kennady R.V., Thirumurthi K., Veeramalla Divya, Dimalie Michui (2026) studied this question.
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