Spatio-temporal patterns and spatial autocorrelation of chandipura virus outbreaks in India (2009–2023): A one health perspective

The Chandipura virus, a sandfly-borne pathogen linked to Acute Encephalitis Syndrome (AES), has emerged as a critical public health concern in India. Despite its sporadic detection, major outbreaks in 2014 and reports of AES cases in 2024 highlight its potential for severe disease burden. Driven by monsoon seasonality, sandfly ecology, deforestation, and human-livestock interactions, the Chandipura virus underscores the intricate interplay between human, animal, and environmental health. Understanding its spatio-temporal patterns is crucial for early warning systems and targeted interventions, making a One Health approach essential. This study analyzed national-level weekly surveillance data (2009–2023) from the Integrated Disease Surveillance Programme (IDSP) to examine the spatial and temporal trends of Chandipura virus outbreaks in India. While initial reports in 2024 documented AES cases from multiple states, analysis of IDSP data confirmed that all reported Chandipura outbreaks during 2009–2023 were confined to Gujarat. Using Geographic Information Systems (GIS) and spatial analytics in R (version 4.3.2), we applied Moran’s I and Geographically Weighted Regression (GWR) to assess spatial clustering and environmental associations. Results confirmed a strong monsoonal seasonality, with outbreaks peaking in 2014 and 2019. Hotspots were located in eastern and central Gujarat, particularly Dahod, Panchmahal, and Vadodara, with Local Moran’s I indicating significant clustering. Global Moran’s I (0.46, p < 0.001) confirmed spatial autocorrelation, and GWR demonstrated a strong positive correlation between forest density and outbreak distribution, suggesting ecological drivers of transmission. This study provides the first comprehensive spatio-temporal analysis of Chandipura virus outbreaks using national surveillance data. Findings underscore the need for sustained surveillance, ecological risk mapping, and integrated vector control strategies under a One Health framework. GIS-enabled predictive modelling can enhance preparedness and localized response strategies to mitigate future outbreaks. • Breakthrough Temporal Insights: Chandipura virus outbreaks exclusively occurred during the monsoon season, with distinct peaks in 2014 and 2019—establishing a predictable pattern that underscores the need for season-specific disease preparedness and response strategies. • Revealing High-Risk Clusters: Moran’s I spatial analysis identified hotspots in Dahod, Panchmahal, Kheda, and Vadodara, signifying areas with persistent outbreak risks. This evidence provides critical guidance for targeted interventions and resource allocation. • Decoding Environmental Triggers: A robust positive correlation between forest cover and outbreak intensity suggests that ecological variables significantly influence virus transmission. This finding reinforces the urgent need for eco-driven vector control strategies. • Harnessing Cutting-Edge GIS & Statistical Techniques: The study employs Geographic Information Systems (GIS) and Geographically Weighted Regression (GWR), powered by R software (v4.3.2), demonstrating the transformative potential of geospatial analytics in epidemiological intelligence. • Public Health Game-Changer: Our findings offer a roadmap for precision public health, advocating for localized, high-impact interventions in ecologically vulnerable regions, with an emphasis on One Health-based mitigation approaches. • Pioneering Vector-Borne Disease Research: This research sets a benchmark in integrating GIS into outbreak surveillance, paving the way for innovative spatial epidemiology frameworks in resource-limited settings. • Why It Matters: This study redefines our understanding of Chandipura virus epidemiology, equipping policymakers and public health leaders with data-driven, proactive strategies to combat future outbreaks effectively.