Panel Data Modelling for determining Climate Change impact on Dengue in India from 1997-2024: A Data Science Project

Background & objectives: Understanding the interplay between climate, socio-demographic factors, and dengue incidence is critical for designing targeted interventions. This study examines the relationship between climatic variables, socio-demographic indicators, and annual reported dengue cases across Indian states from 1997 to 2024. Methods: An ecological study design was adopted, utilizing a data science approach with open-source datasets. A comprehensive panel dataset was compiled, integrating state-wise annual dengue cases from The OpenDengue TM project and climatic variables (MERRA-2 global climatic models) using API-based data pipelines. Socioeconomic factors were retrieved from the Census of India (Population projections calculated), and MOSPI, GoI (Gross Domestic Product). Exploratory Data Analysis, followed by Panel regression modelling was carried out. The analysis was carried out using R version 4.5.2. Results: A total of 17,94,512 dengue cases were analyzed. A significant increase in annual dengue incidence rates and variations across climatic regions and states were observed. There was a significant correlation of Dengue with temperature, precipitation, and GDP (r = 0.2, 0.5, and 0.7 respectively, p < 0.05). One way fixed effects within estimator panel model showed that a unit increase in temperature and precipitation adjusted for other variables increase dengue rates by 13 and 7 per cent respectively (adjusted R2 = 0.49, p < 0.001) with a positive non-significant spatial auto correlation at state level. Interpretation & conclusion: This study highlights the critical role of climate change and urbanization in shaping India's dengue burden. Findings emphasize the urgency of interventions for climate change to mitigate the impact of dengue in vulnerable regions.