Spatial Analysis of Malaria in Bangladesh: Insights from Bayesian Disease Mapping Models

Background and aims Malaria remains a public health concern in Bangladesh, despite a notable decline in reported cases since 2012 and a brief resurgence in 2014. Aligned with the United Nations Sustainable Development Goal (SDG) 3.3, which targets the elimination of malaria by 2030, Bangladesh has implemented multiple control and prevention strategies. This study assesses the significance of the decline in malaria cases and applies Bayesian hierarchical models to capture spatial dynamics of malaria-related vulnerability, map district-level risk, and inform targeted interventions. Methods A nationwide spatial analysis was conducted using district-level malaria data from the Bangladesh Disaster-related Statistics (BDRS) 2021, which report cumulative counts of “population suffering from malaria due to disaster” for 2015–2020. These data were used as a proxy indicator of relative malaria vulnerability. National malaria time-series data were obtained from Bangladesh’s National Strategic Plan for Malaria Elimination (2021–2025) published by the Asia Pacific Malaria Elimination Network (APMEN), together with malaria surveillance summaries from the World Malaria Reports (2024,2025) published by the World Health Organization (WHO). District-level rainfall data were obtained from the Bangladesh Water Development Board. Bayesian hierarchical disease mapping models were used to assess spatial dependence and district-level malaria risk. Spatial visualization and autocorrelation analyses (Global Moran’s I, Geary’s C, and Local Moran’s I) were performed using R (version 4.4.0), and Bayesian model estimation was carried out using WinBUGS via Markov Chain Monte Carlo methods. Results The analysis showed evidence of a trend in decreasing malaria cases by a value of −0.691 in the Mann-Kendall trend test. However, spatial analysis revealed significant clustering and geographic heterogeneity. Persistent high-risk clusters were identified in the southeastern hilly regions. Additionally the presence of excess zeros in the data justified the use of zero-inflated models. Conclusion The findings show significant national decline and offer valuable insights into the geographical variability in malaria-related vulnerability in Bangladesh. Rather than being direct indicators of malaria transmission, the results should be understood as representing relative risk patterns based on data related to disasters. Under current data limitations, these results provide evidence to boost spatially informed malaria control methods and assist geographically focused interventions.