Relationship between Weather Elements and the Incidence of Malaria, Measles and Cholera across Six Local Government Areas in Kogi State, Nigeria

This study examined the relationship between weather elements and the incidence of malaria, measles and cholera across six Local Government Areas in Kogi State, Nigeria, namely Adavi, Okene, Bassa, Dekina, Kabba/Bu and Lokoja, using twenty-one years, 2005 to 2025, of monthly meteorological and disease surveillance data. Monthly rainfall, air temperature and relative humidity obtained from the Nigerian Meteorological Agency were related to monthly reported cases of each disease using multiple linear regression, fitted separately for each disease and Local Government Area. For malaria, rainfall, temperature and relative humidity were statistically significant predictors in all six Local Government Areas, with the regression models explaining between 69.1 percent and 85.6 percent of the variation in malaria incidence, the strongest fit recorded in Okene and the weakest in Lokoja. For measles, all three weather elements were likewise significant predictors in every Local Government Area, though the models explained a comparatively modest 25.7 to 35.6 percent of yield variation, reflecting the stronger role of non-climatic factors such as immunization coverage. For cholera, temperature and relative humidity were significant in all six Local Government Areas, while rainfall was significant in four of six, with the models explaining between 54.4 and 64.9 percent of the variation in cholera incidence. Across all three diseases, Okene and Kabba/Bu consistently returned among the highest explanatory power, while Lokoja and Adavi showed comparatively weaker climatic model fits despite high disease burden, suggesting that non-climatic factors play a larger role in these two Local Government Areas. The study concludes that weather elements are statistically significant, spatially varying determinants of malaria, measles and cholera incidence in Kogi State, and recommends that climate-informed early warning systems be calibrated separately for each disease and Local Government Area rather than applied uniformly across the State.

Climate-Health Relationship, Malaria, Measles, Cholera, Multiple Linear Regression

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