Spatio-temporal analysis of dengue outbreaks in India (2021-2025): evidence from the secondary analysis of Integrated Disease Surveillance Programme.

INTRODUCTION: Dengue fever is an emerging public health challenge in India, with outbreaks influenced by environmental factors. Understanding these dynamics is essential to advance from containment to early warning systems. The study aimed to (1) categorising outbreaks by Indian Meteorological Department climatic zones to assess burden; (2) mapping transmission architecture via Global Moran's I, Getis-Ord Gi Hotspot Analysis, spatial network graphs (K-nearest neighbour, distance threshold, minimum spanning trees (MSTs)) and degree centrality and (3) calculating velocity, measured by effective reproductive number (R t ), using Bayesian renewal equation models. METHODS: The study analysed weekly outbreak reports from a national-level report from the Integrated Disease Surveillance Programme between 2021 and 2025. RESULTS: Analysis of 774 outbreaks (35 202 cases; 195 deaths; case fatality rate (CFR): 0.55%) identified transmission patterns. Maharashtra and Kerala accounted for 52.4% of all outbreaks, with South Peninsular states recording pre-monsoon transmission windows. Rural transmission correlated with a CFR (3.08%) compared with urban clusters (0.29%), indicating case ascertainment bias. MST topology identified 'super-spreader' district hubs that served as focal points for dissemination. These hubs accelerated transmission with effective reproductive number (R t ) at 4.0 within a 3-week period, representing a 1.38-fold increase relative to state-level baseline R t estimates. CONCLUSION: Integrating syndromic data with climatic classifications, spatial network topology and Bayesian R t modelling provides a framework for understanding disease spread and informing targeted public health interventions across low- and middle-income countries.