Spatio-temporal analysis of dengue cases in Sabah

Introduction: Dengue fever is a significant public health issue worldwide. Geographic Information System is a powerful tool in public health, allowing for the analysis and visualisation of spatial data to understand disease distribution and identify clusters of cases. Therefore, this study aims to determine the spatiotemporal distribution of dengue cases in Sabah. Methods: Quantum Geospatial Information System (QGIS) and GeoDa software were used to determine the spatial distribution, pattern, and cluster analysis. Results: The spatial distribution of dengue cases shifted, with most cases concentrated on the east coast of Sabah. The distribution of dengue cases in Beluran, Tenom, Kota Marudu, Kudat, Keningau, and Papar changed from 2017 to 2020. The scatter plots of Moran’s index values were generated to analyse the spatial clustering of dengue cases in Sabah over four years: 2017 (Moran’s index = 0.271), 2018 (Moran’s index = 0.333), 2019 (Moran’s index = 0.367), and 2020 (Moran’s index = 0.294). The statistical significance of clustering was established by observing p-values below the threshold of 0.05 for all four years. Local indicators of spatial association showed the spatial autocorrelation pattern of high-high (hotspot) areas with elevated dengue incidence and low-low (cold-spot) areas with relatively lower dengue rates. Conclusion: This study has provided evidence of dengue case distribution patterns, spatial clustering, and hotspot and coldspot areas. Prioritising these clusters can improve planning and resource allocation for more efficient dengue prevention and control.