Temporal patterns of climate variability and malaria incidences among children (0-5) years in Uganda: A Time Series analysis.

Background Malaria remains a major public health challenge in Uganda, particularly among children under five years of age. However, analysis of the data on malaria has been focused on a single variable while the impact of climate variation on malaria is over several factors and time. Our study assesses the temporal patterns of climate variability and malaria incidence among children aged 0–5 years in Uganda using a time series analysis Methods and material The study used 150 monthly time series records between 2015 and 2022. It used the VECM approach which allows for the investigation of both short-term changing aspects and long-term relationships among the variables. The variables under the study included confirmed malaria cases, rainfall, minimum and maximum temperatures, and the vegetation cover. The study obtained data from the ministry of health/DHIS2, NASA Earth Data, CHIRPS, and NASA EOSDIS. Results The results revealed significant long-term relationships and short-term feedback mechanisms between malaria incidence and climatic factors. The error correction term (ECT) for malaria was -0.006, indicating a slow adjustment to equilibrium. In contrast, rainfall, minimum temperature, and the NDVI showed correction behaviors, adjusting upward following deviations. Short-term changing aspects revealed that previous values of malaria cases (coefficient = 0.091) and rainfall (coefficient = 0.061) positively influenced current malaria trends. The minimum temperature displayed strong autocorrelation (coefficient = 0.810), whereas the NDVI showed a large short-term response (coefficient = 140.100), highlighting its sensitivity to environmental shifts. Maximum temperature had a negative short-term association with malaria incidences (coefficient = -0.259), suggesting inverse seasonal effects. Conclusions The study reveals significant short-term and long-term interactions among malaria cases, rainfall, temperature, and NDVI. The presence of statistically significant error correction terms indicates that the system adjusts to restore equilibrium following deviations, with malaria cases exhibiting consistent correction. Lagged coefficients show that past changes, particularly in minimum temperature and NDVI, exert a strong influence on current conditions.