Seasonality, Temporal Persistence and Climate Sensitivity of Malaria Occurrence in Kaduna North Nigeria
DOI:
https://doi.org/10.47514/kjg.2026.08.01.071Keywords:
Keywords: malaria; climate variability; relative humidity; seasonal Mann–Kendall; negative binomial regression; Kaduna North; NigeriaAbstract
In this study, monthly data on malaria incidence and climate variables in the Kaduna North Local Government Area of Nigeria were investigated between January 2018 and December 2025. The variables included monthly rainfall, relative humidity (RH), maximum temperature (Tmax), minimum temperature (Tmin), and malaria prevalence. These variables were analyzed using descriptive climatology, the Seasonal Mann–Kendall test, the Seasonal Sen slope test, lagged Pearson and Spearman correlation, Seasonal and trend adjustment with false discovery rate correction, and negative binomial regression with autoregressive terms. Seasonal differences were evaluated using Welch’s t test, the Mann–Whitney U test, effect size, and STL decomposition. There was a significant decrease in rainfall (p=0.0023), but there was an increase in Tmax (Z=3.393, p=0.0007; seasonal Sen slope ≈0.119 °C per year). There was a strong increase in malaria incidence from 5,688 cases in 2018 to 48,112 cases in 2024 and a decline to 32,673 in 2025. After common seasonality and trends were removed, RH (lag 0) and Tmin (lag 2 months) were significant for malaria. In the final autoregressive negative binomial model, only the RH remained significant (IRR=1.053, 95% CI 1.018–1.089, p=0.0024), but this association disappeared when 2025 was excluded, indicating sensitivity to anomalous RH observations. Previous-month malaria markedly improved the model fit and removed residual serial dependence. The wet-season malaria cases averaged 2,499.75 cases, which was 2.44 times the dry-season mean. These findings indicate that malaria dynamics in Kaduna North are dominated by seasonality and temporal persistence, with climate effects that are context- and model-dependent. Climate-informed surveillance should therefore combine climatic indicators with recent case history and seasonal preparedness.
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