Long-term associations between seasonal variability and malaria transmission in Bangladesh: a 16-year national time-series analysis

Abstract

Malaria transmission is strongly influenced by climatic conditions that regulate Anopheles mosquito survival and parasite development. However, existing studies in Bangladesh are largely short-term or region-specific, limiting understanding of long-term climate–malaria dynamics. This study addresses this gap by examining national-level, long-term associations between malaria incidence and climatic factors. A retrospective ecological time-series study was conducted using monthly malaria surveillance data from Bangladesh between January 2008 and December 2023. Climatic variables—rainfall, temperature, relative humidity, and sunshine duration—were obtained from the Bangladesh Meteorological Department for the same period ensuring full temporal alignment with malaria data. Descriptive analyses were used to assess long-term trends and seasonality. Pearson correlation was used to assess linear relationships between variables, while Spearman correlation was applied to capture potential monotonic but non-linear associations and reduce sensitivity to outliers. Multiple linear regression models incorporating one-month lagged variables were developed to identify independent climatic predictors of transmission. A total of 192 monthly observations were analyzed. Malaria incidence showed a marked long-term decline, yet a consistent seasonal pattern persisted, with transmission peaking during the monsoon months (July–September). Mean relative humidity demonstrated the strongest positive correlation with malaria incidence (p < 0.001), followed by lagged rainfall (p < 0.01). In multivariate analysis, mean humidity, lagged rainfall, and lagged sunshine duration remained significant predictors, while temperature was not independently associated. The regression model accurately captured seasonal timing but underestimated peak magnitude during outbreak years. Despite declining incidence, malaria in Bangladesh retains a stable, climate-driven seasonal signature. Climatic variables are effective in predicting transmission timing but insufficient to explain outbreak intensity. Integrating climate indicators into surveillance systems may strengthen preparedness and support malaria elimination efforts in residual transmission settings.