Background The state of Goa, a popular tourist destination located on India’s western coast, has seen a steep increase in dengue cases in the last decade. Systematic characterization of case trends, serotypes, affected demographics, spatiotemporal clusters and hotspots, and environmental determinants was undertaken to guide evidence-based dengue prevention and control policies in Goa and across western India. Method A health center level spatiotemporal analysis of dengue from 2011–2024 was performed using passive surveillance data routinely collected from all 34 health facilities through the National Vector Borne Disease Control Programme, Directorate of Health Services, Goa. The dengue trends were analyzed using the Seasonal Mann-Kendall (SMK) test. The space-time trends, dengue case clusters (high and low transmission zones across Goa), and forest-based forecasting were performed using the space-time pattern mining framework in ArcGIS Pro 3.x. The negative binomial generalized linear model with log link was used to quantify location effects. The correlation between climate change and rising dengue incidences was determined using a distributed lag non-linear model. Results The dominant dengue virus serotype in Goa was DENV-2, accounting for 58.6% of infections, followed by DENV-1 (21.51%), DENV-3 (17.87%), and DENV-4 (2.03%). The clusters of dengue cases were predominantly observed in North Goa—the SMK test showed a significant positive trend across 16 of the 17 health facilities in the district. The space-time analysis showed a significant monotonic increase (standardized Mann-Kendall Z-statistics ≈ 3.94; p < 0.001) of dengue cases in recurrent high-high clusters in Candolim, Porvorim, Siolim, Pernem, Panaji, Saligao, Mapusa, and Vasco health centers. Forest-based forecasting for 2025–2029 predicts consistent caseloads in the high-high cluster, with an average annual increase of ~21% expected cases. The regression model showed the significance of climatic variables with a lag period of 2–3 months and a total annual rainfall threshold of 607 mm in North Goa and 630 mm in South Goa. Rainfall above this threshold could lead to higher dengue transmission. Conclusion Integrating space-time analytics, negative binomial modelling, and climate-lagged associations produced operationally useful risk maps and short-term forecasts. These outputs justify pre-monsoon source reduction, targeted vector control, serotype-guided surveillance, and climate-informed early warning for dengue in Goa and comparable settings in western India.
Balasubramani et al. (Mon,) studied this question.