Abstract:
This study examined the spatiotemporal distribution of dengue incidence in the Puttalam District, Sri Lanka, from 2013 to 2023. The study is novel because it integrates time series analysis, two-way ANOVA, and GIS-based Inverse Distance Weighting (IDW) interpolation to examine temporal and spatial variations in dengue incidence across all 14 Medical Officer of Health (MOH) divisions of the district. Secondary annual dengue incidence data (154 observations: 14 MOH divisions × 11 years) obtained from the Ministry of Health, Sri Lanka, were analysed. Time series analysis and linear trend analysis were used to assess temporal patterns, while two-way ANOVA was performed to evaluate the effects of year and MOH division on dengue incidence. Spatial distribution and hotspot areas were identified using IDW interpolation in ArcGIS 10.8. The findings revealed an overall increasing trend in dengue incidence during the study period. District-wide incidence increased substantially after 2016, reached its highest level with more than 5,600 reported cases in 2017, declined during 2018–2021, and increased again in 2022 and 2023. Two-way ANOVA showed that both year (F = 8.166, p < 0.001) and MOH division (F = 5.073, p < 0.001) had statistically significant effects on dengue incidence, indicating significant temporal and spatial variation. Spatial analysis identified persistent dengue hotspots in the Puttalam, Kalpitiya, Chilaw, Wennappuwa, and Dankotuwa MOH divisions, demonstrating the expansion and persistence of high-risk areas within the district. The findings indicate that dengue incidence exhibits clear spatial clustering and temporal variability in Puttalam District. Although environmental and socio-economic factors such as urbanisation, inadequate waste management, and favourable mosquito breeding conditions may contribute to the spatial distribution of dengue, these relationships were not statistically examined in the present study. Therefore, dengue prevention efforts should prioritise hotspot MOH divisions through GIS-based surveillance, pre-monsoon vector control, improved drainage and solid waste management, and communitybased source reduction programmes. These findings provide evidence to support location-specific dengue control strategies and future research integrating climatic and environmental variables for improved disease prediction.
