Malar J. 2026 Feb 7. doi: 10.1186/s12936-026-05824-0. Online ahead of print.
ABSTRACT
BACKGROUND: Malaria remains a major global public health concern with the greatest burden in tropical and subtropical regions, particularly sub-Saharan Africa. Uganda ranks among the world’s highest burden countries, with its warm temperatures, abundant rainfall and diverse mosquito breeding habitats sustaining year-round malaria transmission in malaria endemic areas. This study assessed malaria incidence trends and their association with climate variables in Yumbe district, Uganda.
METHODS: A retrospective ecological time-series study analysed malaria incidence (2017-2021) in Yumbe district, Uganda, using District Health Information System reports and Uganda National Meteorological Authority climate data (daily temperature and rainfall). Data were cleaned in Excel and analysed in R software V4.5.1. Monthly/annual summaries, seasonal pattern graphs, Kendall’s tau correlations for non-linear associations, and Multiple Linear and Poisson regressions with lag effects were done. Time series analysis involved seasonal decomposition, cross-correlation, and ARIMAX modelling. A multivariable OLS regression on log(1 + cases) with best-lagged rainfall and minimum temperature further assessed climate influence.
RESULTS: Between 2017 and 2021, a total of 2,066,711 malaria cases were reported in Yumbe district. Malaria trends closely followed rainfall patterns, peaking during the period of high precipitation. Time-series analysis showed that rainfall was positively associated with malaria incidence at one-month lag (β = 0.38, p < 0.05), while minimum temperature was inversely associated (β = – 0.29, p < 0.05). Statistical analysis revealed rainfall (mm) strongly led malaria cases by 1 month (r = 0.759, p < 0.001). Maximum temperature showed no significant effect on malaria incidence.
CONCLUSION: Malaria incidence in Yumbe district is strongly influenced by rainfall and minimum temperature. This study highlights the role of climate variability in malaria transmission in malaria endemic areas. Integrating climate data into surveillance and early warning systems could enhance timely interventions in malaria endemic areas like Yumbe district.
PMID:41654939 | DOI:10.1186/s12936-026-05824-0
