Publication

Abstract : Eliminating canine-mediated human rabies deaths by 2030 is a global priority, necessitating a data-driven approach to understand rabies dynamics and implement effective prevention strategies. This study provides epidemiological insights into canine rabies in Chennai, analyzing nine years of surveillance data (n = 428, March 2010 – February 2019) to assess trends, seasonality and predictive patterns. Change point and time series analyses were conducted and forecasting models were evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE) metrics. No significant seasonality was detected, but change point analysis identified two key shifts, segmenting the data into three phases and revealing an overall declining trend. Among the models tested, the Prophet model demonstrated the best predictive performance (RMSE: 1.88, MAE: 1.55, MAPE: 45.44 %, MASE: 3.52), outperforming the Generalized Additive Model (GAM), Bayesian Structural Time Series (BSTS) and Seasonal Trend decomposition using Loess combined with ARIMA (STL + ARIMA (0,0,2)). This study offers critical epidemiological insights for strengthening One Health-based rabies control strategies, particularly in urban settings where canine rabies plays a major role in human exposure risk. By providing longitudinal data and predictive modelling, these findings guide targeted preventive interventions, inform evidence-based policy decisions and support global efforts to eliminate dog-mediated human rabies deaths by 2030.