Modelling the Influence of Temperature on Malaria Incidence in Abuja, Nigeria: Short- and Long-Term Effects of Temperature

Abstract

Malaria remains an important public health concern, and climatic conditions are recognized as important environmental determinants of malaria transmission. Temperature can influence mosquito survival, parasite development and vectorial capacity; however, the relationship between temperature and malaria incidence may vary spatially and temporally. This study assessed the relationship between mean temperature and malaria incidence across selected Area Councils of the Federal Capital Territory, Nigeria. Data covering the period 2020–2022 were examined to determine temporal and spatial patterns of malaria occurrence. Longer-term malaria incidence data were also examined using annual malaria incidence estimates and population projections. Climatic variables, particularly temperature was considered in assessing variations in malaria incidence. Malaria incidence was expressed in relation to population size to facilitate comparison among the Area Councils. The findings generally showed an inverse relationship between mean temperature and malaria incidence. Correlation coefficients varied from weak to strong negative associations across the different analyses, indicating that higher mean temperatures were generally associated with lower malaria incidence. Regression coefficients were also predominantly negative, with values ranging from approximately −0.194 to −0.607, indicating weak to moderate inverse relationships in the respective analyses. However, the long-term regression models for the six Area Councils demonstrated very limited explanatory power, with R² values ranging from approximately −4% to 1%. The corresponding ANOVA results indicated that the regression models were not statistically significant at the 5% significance level. The negative R² values observed for some locations indicate that the models performed worse than an intercept-only model based on the mean malaria incidence. Malaria early-warning systems should therefore incorporate local climatic and epidemiological conditions rather than applying a uniform temperature threshold across all locations. Consistent, highquality and spatially disaggregated malaria data should be maintained at health facilities and public health institutions. Accurate records of confirmed malaria cases, diagnostic methods, population at risk and seasonal patterns would improve the reliability of future climate–malaria analyses.