Modelling an Optimal Climate-Driven Malaria Transmission Control Strategy to Optimise the Management of Malaria in Mberengwa District, Zimbabwe
A Multi-Method Study Protocol
Bibliographic Data
| ID | 15498844 |
|---|---|
| Authors | Tafadzwa Chivasa (Ministry of Health and Child Welfare), Mlamuli Dhlamini (0000-0002-5857-5734, National University of Science and Technology), Auther Maviza (0000-0002-5153-9212, University of the Witwatersrand), Wilfred Njabulo Nunu (0000-0001-8421-1478, University of Botswana, corresponding author), Joyce Mahlako Tsoka-Gwegweni (0000-0002-4888-1973, University of the Free State) |
| Year | 2025 |
| Volume | 22 |
| Issue | 4 |
| Pages | 591-591 |
| Publication date | 2025-04-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph22040591 |
| PMID | 40283815 |
| OpenAlex | W4409335346 |
| Language | EN |
| Citations received | 2 |
| References cited | 49 |
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
Cartography · Climate change · Context (archaeology · Environmental health · Environmental planning · Environmental resource management · Geographic information system · Geography · Geospatial analysis · Malaria · Population · Environmental Science · Malaria Research and Control · Medicine · Mosquito-borne diseases and control · Species Distribution and Climate Change · Ecology
Synthesising qualitative and quantitative evidence
Impact of climate change on global malaria distribution
The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500
Rayyan—a web and mobile app for systematic reviews
Spatial and spatio-temporal methods for mapping malaria risk
The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers
The burden of malaria in sub-Saharan Africa and its association with development assistance for health and governance
Climate Change and Health Preparedness in Africa
Assessment of malaria as a public health problem in and around Arjo Didhessa sugar cane plantation area, Western Ethiopia
The treatment of incomplete data
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |