Naveenkumar Viswanathan, Bharathi Mangalanathan Vijaya, Kannan Porteen, Nag B S Pradeep, Sundaram Sureshkannan, Mohanadasse Nithya Quintoil, Amachawadi Raghavendra G, Dubey Muskan, Cull Charley A, Shivamallu Chandan, Kollur Shiva Prasad, Veeranna Ravindra P
Veterinary Clinical Complex, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Udumalpet, Tiruppur 642 205, Tamil Nadu, India.
Department of Veterinary Public Health and Epidemiology, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Salem 636 112, Tamil Nadu, India.
One Health. 2025 Jul 4;21:101128. doi: 10.1016/j.onehlt.2025.101128. eCollection 2025 Dec.
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 ( = 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.
到2030年消除犬介导的人类狂犬病死亡是一项全球优先事项，这需要采用数据驱动的方法来了解狂犬病动态并实施有效的预防策略。本研究提供了对金奈犬狂犬病的流行病学见解，分析了九年的监测数据（n = 428，2010年3月至2019年2月），以评估趋势、季节性和预测模式。进行了变化点和时间序列分析，并使用均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和平均绝对尺度误差（MASE）指标评估了预测模型。未检测到明显的季节性，但变化点分析确定了两个关键转变，将数据分为三个阶段，并揭示了总体下降趋势。在测试的模型中，先知模型表现出最佳的预测性能（RMSE：1.88，MAE：1.55，MAPE：45.44%，MASE：3.52），优于广义相加模型（GAM）、贝叶斯结构时间序列（BSTS）和使用局部加权回归结合自回归积分移动平均（STL+ARIMA（0,0,2））的季节性趋势分解。本研究为加强基于“同一健康”的狂犬病控制策略提供了关键的流行病学见解，特别是在犬狂犬病在人类暴露风险中起主要作用的城市环境中。通过提供纵向数据和预测模型，这些发现指导有针对性的预防干预措施，为基于证据的政策决策提供信息，并支持到2030年消除犬介导的人类狂犬病死亡的全球努力。
