Cluster analysis of the spatial distribution of cholera in Uganda.

backgroundOver the past few decades, cholera has remained a global public health challenge, with Africa bearing a disproportionate share of its burden. Uganda has experienced a notable decline in the incidence of cholera over the past decade, largely attributed to intensified public health efforts, including oral cholera vaccination (OCV) campaigns, strengthened surveillance, and targeted WASH interventions. This study seeks to provide information on spatial clustering patterns of cholera incidence and evaluate how these patterns varied across selected outbreak years.

methodsDistrict-level cholera case data (2012–2022) were obtained from the Ministry of Health with the support of ministry officials. The data were combined with Uganda Bureau of Statistics (UBOS) population projections to compute annual incidence rates per 100,000 people. A spatial statistical analysis was performed in ArcGIS 10.8, whereby Global Moran’s I was used to evaluate overall clustering, followed by Anselin Local Moran’s I for years demonstrating significant spatial autocorrelation. Annual cluster outputs were compared longitudinally to assess shifts in cluster distribution.

resultsThe analysis revealed significant spatial clustering of the incidence of cholera (global Moran’s I = 0.0967, Z = 2.89, p = 0.0038), (global Moran’s I = 0.203278, Z = 5.16, p = 0.0000), (global Moran’s I = 0.066294, Z = 1.76, p = 0.079080), (global Moran’s I = 0.171727, Z = 6.76, p = 0.0000) and (global Moran’s I = 0.069635, Z = 2.01, p = 0.044825) for the years 2012, 2013, 2015, 2019 and 2022, respectively, confirming nonrandom geographic patterns for these years. High-incidence cholera clusters occurred along Lake Albert, Lake Victoria, and the Nile and were linked to fishing communities, mobility, flooding, and poor sanitation. Clusters shifted from the western and northern districts to the eastern border areas over time. The spatial outliers revealed districts with unusually high or low incidence rates, highlighting dynamic, heterogeneous transmission patterns.

conclusionThe spatial analysis identified subtle yet significant cholera clusters across the study period, but the affected districts changed over time. Rather than persistent locations, different districts emerged with high incidence rates in different periods, often neighboring similarly affected districts, indicating shifting clustering patterns. The concentration was around lakes, rivers, and international borders, highlighting ongoing water, sanitation, and hygiene (WASH) gaps and cross-border transmission. Targeted surveillance, improved water and sanitation in high-risk zones, and stronger multisectoral preparedness remain essential for sustainable cholera control in affected communities and regions.