Abstract
About this talk
Background: This study investigated the role of climate variability in shaping malaria incidence by analysing malaria incidence trends and their relationship with climatic variables.
Setting: The study was conducted in the 7 districts of Manicaland Province, Zimbabwe.
Methods: This study employed a quantitative ecological research design to analyse spatio-temporal patterns of malaria incidence. Environmental covariates—including normalised difference vegetation index, land surface temperature, and rainfall—were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Climate Hazards Group infrared Precipitation with Station data (CHIRPS) datasets, processed using ArcMap to generate annual district-level means. Data were aggregated at the district scale and analysed using descriptive statistics to visualize temporal and spatial trends. Pearson’s correlation and simple linear regression analyses were conducted to assess the strength and predictive relationships between malaria incidence and environmental variables, with significance evaluated at α = 0.05.
Results: Malaria incidence showed marked spatio-temporal variability. Rainfall exhibited a strong positive association with malaria incidence (r = 0.87, R² = 0.75, p < 0.01), while NDVI also showed a strong positive relationship (r = 0.91, R² = 0.62, p < 0.001). Temperature displayed a moderate but statistically non-significant association (r = 0.58, p = 0.081), suggesting that rainfall and vegetation dynamics are more influential drivers of malaria patterns in the province.
Conclusions: Temperature showed a moderate but non-significant association with malaria incidence, while rainfall and vegetation density were strongly and significantly associated, indicating that rainfall and vegetation are key environmental drivers of malaria transmission.
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