Modelling an Optimal Climate-Driven Malaria Transmission Control Strategy to Optimise the Management of Malaria in Mberengwa District, Zimbabwe: A Multi-Method Study Protocol

Tafadzwa Chivasa, Mlamuli Dhlamini, Auther Maviza, Wilfred Njabulo Nunu () and Joyce Tsoka-Gwegweni
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Tafadzwa Chivasa: Department of Environmental Health, Faculty of Environmental Science, National University of Science and Technology, Bulawayo 00263, Zimbabwe
Mlamuli Dhlamini: Department of Applied Mathematics, Faculty of Applied Science, National University of Science and Technology, Bulawayo 00263, Zimbabwe
Auther Maviza: Department of Environmental Science, Faculty of Environmental Science, National University of Science and Technology, Bulawayo 00263, Zimbabwe
Wilfred Njabulo Nunu: Department of Environmental Health, Faculty of Environmental Science, National University of Science and Technology, Bulawayo 00263, Zimbabwe
Joyce Tsoka-Gwegweni: Department of Public Health, Faculty of Health Sciences, University of the Free State, Bloemfontein 0027, South Africa

IJERPH, 2025, vol. 22, issue 4, 1-19

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.

Keywords: climate change; malaria transmission; management; Geographic Information System; Mberengwa; Zimbabwe (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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