Impact of projected climate and socioeconomic scenarios on state-wise annual dengue incidence in India using ensemble models

India, the world's most populous country, has reported over 1 million dengue cases and ~3,000 deaths between 2007 and 2022. With the annual state-wise data, we examined the spatiotemporal distribution of dengue in 28 states and eight union territories for 16 years across India. Using state-wise data on climatic variables, socio-economic inequities and land-use land-cover changes, potential determinants for the state-wise transmission were identified through a supervised regression model. The identified determinants were then mapped to various novel developmental scenarios, which were designed based on the existing shared socio-economic pathways. To estimate the dengue burden for each scenario, ensemble models of XGBoost and Gradient Boosting regression algorithms were developed. We note that 73% of the cases occurred between 2016 and 2022, highlighting a significant increase in dengue outbreaks across the country. All Himalayan states, which witness colder temperatures, have witnessed a growth in cases: Himachal Pradesh reported 168 times more cases between 2016 and 2022 than those observed between 2007 and 2015. The models suggest that dengue incidences may potentially change under future socioeconomic burden, although projections are associated with substantial uncertainty and should be interpreted as potential trajectories rather than definitive forecasts. We estimate that development focused on sustainability (874.2 per 10 million; 95% CI: 535.4, 1212.9) and fossil fuels (888.02 per 10 million; 95% CI: 521.2, 1254.9) will relatively cause a lesser burden across the country by the 2030s. Southern states are projected to have higher dengue outbreaks, while Jharkhand, a historically malaria-endemic state, is estimated to report twice as many cases in 2050 as what was reported in 2022. Given the uncertainty associated with long-term projections, public health strategies may benefit from adaptive approaches which are backed by climate- and socioeconomic-data integrated early warning systems that can respond to evolving climatic and socioeconomic conditions influencing dengue transmission. Our study provides insights into how the spread of dengue will change with varying models of socio-economic development, which highlights the spatial heterogeneity in potential future dengue risk, suggesting that resource allocation and surveillance efforts may benefit from region-specific prioritisation instead of a uniform policy.