Naveenkumar Viswanathan, Bharathi Mangalanathan Vijaya, Kannan Porteen, Selvaraju Ganapathy, Nag B S Pradeep, Vijayarani Kumanan
Veterinary Clinical Complex, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Udumalpet, Tiruppur, Tamil Nadu 642 205, India.
Department of Veterinary Public Health and Epidemiology, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), Salem, Tamil Nadu 636 112, India.
Prev Vet Med. 2025 Sep;242:106573. doi: 10.1016/j.prevetmed.2025.106573. Epub 2025 May 16.
Canine Parvovirus (CPV) poses a significant threat to young puppies globally, leading to high morbidity and mortality rates. Despite its widespread impact, research on the influence of climate on CPV outbreaks is limited. This study aimed to analyze risk factors, investigate climatic associations and develop forecasting models for Canine Parvoviral Enteritis (CPVE) using eight years of data from the Teaching Veterinary Hospital at Madras Veterinary College, Chennai, Southern India. Among 6105 suspected cases, 4258 dogs were diagnosed with CPVE, resulting in a positivity rate of 69.75 %. The study identified winter season and July month as periods with higher incidences of CPVE outbreaks. Monthly data analysis revealed a positive correlation between CPVE occurrence and maximum temperature (lagged by 11 months), morning relative humidity (lagged by 1 month) and rainfall (lagged by 10 months). Conversely, negative correlations were found with maximum temperature (lagged by 1 & 6 months), minimum temperature (lagged by 4 months), morning relative humidity (lagged by 7 months), evening relative humidity (lagged by 7 months), rainfall (lagged by 2 & 7 months) and wind speed (lagged by 4 months). For predictive analysis, the Autoregressive Integrated Moving Average with eXogenous variable (ARIMAX) model performed best, incorporating two and seven-month lagged rainfall values and four-month lagged wind speed data. Similarly, the Extreme Gradient Boosting (XGBoost) and Recurrent Neural Network (RNN) models, though not considering climatic data, also demonstrated optimal performance. This study emphasizes the critical need for continuous global monitoring of CPVE and underscores the significant impact of climate on its outbreaks. These findings are crucial for shaping effective intervention strategies to mitigate the impact of CPVE on canine populations.
犬细小病毒（CPV）对全球幼犬构成重大威胁，导致高发病率和死亡率。尽管其影响广泛，但关于气候对CPV爆发影响的研究有限。本研究旨在利用印度南部钦奈马德拉斯兽医学院教学兽医医院的八年数据，分析犬细小病毒性肠炎（CPVE）的风险因素，调查气候关联并开发预测模型。在6105例疑似病例中，4258只犬被诊断为CPVE，阳性率为69.75%。该研究确定冬季和7月为CPVE爆发发生率较高的时期。月度数据分析显示，CPVE发生与最高温度（滞后11个月）、早晨相对湿度（滞后1个月）和降雨量（滞后10个月）呈正相关。相反，与最高温度（滞后1和6个月）、最低温度（滞后4个月）、早晨相对湿度（滞后7个月）、傍晚相对湿度（滞后）、降雨量（滞后2和7个月）和风速（滞后4个月）呈负相关。对于预测分析，带有外生变量的自回归积分移动平均（ARIMAX）模型表现最佳，纳入了两个月和七个月滞后的降雨量值以及四个月滞后的风速数据。同样，极端梯度提升（XGBoost）和递归神经网络（RNN）模型虽然未考虑气候数据，但也表现出最佳性能。本研究强调对CPVE进行持续全球监测的迫切需求，并强调气候对其爆发的重大影响。这些发现对于制定有效的干预策略以减轻CPVE对犬类种群的影响至关重要。
