Spatial epidemiology of dengue and chikungunya in Karnataka using GIS-based analysis

Abstract

Background: Dengue and chikungunya are major vector-borne diseases with significant public health concerns. Understanding their spatial distribution is crucial for effective disease control and prevention strategies. Objective: To analyse the spatial patterns and trends of dengue and chikungunya in Karnataka from 2021 to 2024 using GIS-based methods to identify high-risk districts. Methods: Spatial autocorrelation analysis using Moran’s I and cluster analysis was performed to examine the spatial distribution of dengue and chikungunya incidence rates using GIS-based mapping. Results: A total of 38,229 dengue cases and 8,094 chikungunya cases were recorded during this period, with the highest incidence rates varying across districts. The highest dengue IR in 2024 was recorded in Chikkamagaluru (0.58/1000), Mandya (0.47/1,000), followed by Udupi, while Vijayapura reported the highest chikungunya IR in 2022 (0.17/1,000). Moran’s I result revealed moderate positive autocorrelation for dengue in 2021 and 2023, indicating significant clustering of cases. By comparison, chikungunya exhibited negative spatial autocorrelation in 2023 and a shift to positive in 2024, reflecting a transition from dispersed to more clustered patterns. Conclusion: GIS-based surveillance enhances early outbreak detection, resource allocation, and targeted vector control. Spatial autocorrelation techniques support predictive modelling for other infectious diseases. Strengthening interdisciplinary collaboration, community engagement, and spatial analytics in public health policies supports disease mitigation and urban planning. Globally, integrating spatial epidemiology into surveillance systems can improve outbreak prediction, optimise resources, and guide data-driven policy. These findings highlight the need for GIS-based frameworks and international cooperation to address emerging disease threats effectively.