Local spatial autocorrelation analysis of dengue and dengue hemorrhagic fever in Ho Chi Minh City: Spatial Insights from the 32nd Week

Background : Dengue and dengue hemorrhagic fever (DF/DHF) remain critical public health challenges in Ho Chi Minh City, Vietnam, where recurrent outbreaks highlight the need for fine-scale spatial analysis. Identifying clusters and outliers of disease incidence is essential for guiding effective and targeted control strategies. Methods : Weekly DHF case data for the 32nd epidemiological week of 2025 were aggregated at the commune level. Spatial distribution was first visualized using choropleth mapping and descriptive histograms. A spatial weight matrix was constructed to define commune-level neighborhood structures, and Local Moran’s I (LISA) was applied to detect spatial autocorrelation. The results were interpreted using LISA cluster maps, LISA value distributions, and Moran’s I scatterplots to identify statistically significant hot spots, cold spots, and outliers. Results : The spatial distribution map revealed marked heterogeneity, with higher DHF incidence concentrated in central and southwestern communes, while peripheral areas exhibited lower case numbers. The global Moran’s I was modest but positive (0.192), suggesting localized clustering rather than widespread citywide autocorrelation. LISA results indicated that 14 communes (8.4%) formed high-high clusters, 7 communes (4.2%) formed low-low clusters, and 12 wards (7.2%) were spatial outliers (6 high-low and 6 low-high). Most communes (80.2%) were not significant, indicating that dengue clustering is highly localized. High–high clusters were concentrated in the central-northern urban core, while outliers were located along southern and peripheral districts, reflecting transitional or isolated neighborhood effects. Conclusions : Local Moran’s I analysis demonstrated that dengue transmission in Ho Chi Minh City during the 32nd week of 2025 was characterized by spatially concentrated hot spots and discrete outliers within a largely neutral background. These findings underscore the value of spatial autocorrelation techniques for identifying high-risk neighborhoods, supporting targeted interventions, and enhancing the efficiency of dengue surveillance and vector control programs.