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Evaluation of the Climate Change Impact on Malaria Elimination in Zambia

Domain:

climatehealthcaregeospatial

Record type:

paper
Creator:
MpoMwe
Publisher:
Zenodo
Host:avatar
This review examines the relationship between climate change and malaria transmission in Zambia, with particular focus on how changing temperature, rainfall, and humidity patterns may influence vector ecology, parasite development, transmission dynamics, and the geographical and seasonal distribution of malaria. Evidence was synthesized from published literature, national malaria reports, epidemiological surveys, climate projection studies, geospatial analyses, satellite remote sensing, and malaria monitoring systems covering the early 2000s to 2025. The review integrates epidemiological and climate evidence to assess how ongoing and projected climatic changes may reshape malaria transmission across regions with differing transmission intensities. The findings indicate that rising temperatures, altered rainfall patterns, and variability in humidity are modifying malaria transmission suitability and may extend transmission periods in some areas. Climate change may also increase transmission suitability in previously lower-risk regions by influencing mosquito distribution, breeding habitats, and biting behaviour. Peri-urban communities may be particularly vulnerable, while climatic variability may also affect the effectiveness and timing of vector-control interventions. Despite substantial progress in malaria control in Zambia, climate-related threats occur alongside persistent challenges such as insecticide resistance, health-system limitations, and uncertainty in climate projections. The review highlights the importance of integrating climate-informed malaria surveillance, geospatial technologies, adaptive vector-control strategies, and multisectoral policy approaches into future elimination efforts. Overall, this review emphasizes the need for climate-resilient malaria elimination strategies in Zambia that anticipate changing transmission patterns rather than relying solely on historical epidemiological patterns.

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