ArticleInternational journal of environmental research and public health2025
Modelling an Optimal Climate-Driven Malaria Transmission Control Strategy to Optimise the Management of Malaria in Mberengwa District, Zimbabwe: A Multi-Method Study Protocol.
Article in International journal of environmental research and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predictive Modelling of Climate-Driven Malaria Transmission for Optimal Control Using a Coupled SEIR-SEI and GIS Framework.Public health challenges · 2026Article
- Health Inequalities in Primary Care: A Comparative Analysis of Climate Change-Induced Expansion of Waterborne and Vector-Borne Diseases in the SADC Region.International journal of environmental research and public health · 2025Article
- Impact of Climate Change on Malaria Transmission and Management in Zimbabwe: A Scoping Review of the Literature.Health services insights · 2025Review
- Long-Lasting Insecticide-Treated Net Use Gaps and Severity Predictors in a Pre-Elimination Landscape: A Retrospective Observational Study in Mberengwa, Zimbabwe.Inquiry : a journal of medical care organization, provision and financingObservational
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Malaria is a persistent public health problem, particularly in sub-Saharan Africa where its transmission is intricately linked to climatic factors. Climate change threatens malaria elimination efforts in limited resource settings, such as in the Mberengwa district. However, the role of climate change in malaria transmission and management has not been adequately quantified to inform interventions. This protocol employs a multi-method quantitative study design in four steps, starting with a scoping review of the literature, followed by a multi-method quantitative approach using geospatial analysis, a quantitative survey, and the development of a predictive Susceptible-Exposed-Infected-Recovered-Susceptible-Geographic Information System model to explore the link between climate change and malaria transmission in the Mberengwa district. Geospatial overlay, Getis-Ord Gi* spatial autocorrelation, and spatial linear regression will be applied to climate (temperature, rainfall, and humidity), environmental (Land Use-Land Cover, elevations, proximity to water bodies, and Normalised Difference Vegetation Index), and socio-economic (Poverty Levels and Population Density) data to provide a comprehensive understanding of the spatial distribution of malaria in Mberengwa District. The predictive model will utilise historical data from two decades (2003-2023) to simulate near- and mid-century malaria transmission patterns. The findings of this study will be used to inform policies and optimise the management of malaria in the context of climate change.
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