Unveiling Canine Distemper epidemiology in association with climatic impacts: longitudinal analysis and forecasting in Chennai, India.

Abstract

Canine Distemper (CD) is one of the most dangerous and deadliest viral diseases affecting canines, globally. Despite limited studies on the impact of climate on CD and long-term risk analysis, a study aimed to analyze risk factors, explore climatic links and develop forecasting models using eight years of data from the Teaching Veterinary Hospital at Madras Veterinary College, Chennai, India. Out of 1447 suspected cases, 1185 dogs were clinically diagnosed with CD, representing a positivity rate of 81.89% and various demographic risk factors were examined. Winter season and November month were found to be more susceptible for occurrence of CD. Cross-correlation analysis demonstrated associations (both positive and negative) between temperature, humidity and CD occurrence. On prediction analysis, Autoregressive Integrated Moving Average with eXogenous variable (ARIMAX) model with seven months lags maximum temperature and four months lags evening relative humidity and Recurrent Neural Network (RNN) model with seven months lags maximum temperature exhibited superior performance, whereas Extreme Gradient Boosting (XGBoost) model without climatic data was found to be optimal. This study emphasizes the importance of continuous global surveillance of CD and suggests that its findings will be invaluable for devising intervention strategies.