Optimizing Veterinary Health Management with Deep Learning: Predictive Modeling and Disease Surveillance Using Animal Health Monitoring Data

Abstract

This study explores the use of deep learning techniques for predictive modeling and disease surveillance in veterinary health, utilizing data from the Global Animal Disease Information System (EMPRES-i) to forecast infection counts based on key environmental and disease-specific variables. A simple regression model was applied to predict infection rates using features such as temperature, rainfall, disease type, and region. The model demonstrated high predictive accuracy, with an R-squared value of 0.85 on the test set, indicating that it captured 85% of the variability in infection counts. Key findings showed a strong correlation between temperature and infection rates, underscoring the importance of environmental factors in disease prediction. Despite the model’s strengths, limitations were noted in handling non-linear relationships, suggesting that future work could benefit from more advanced deep learning models. This research highlights the potential of predictive analytics in veterinary health, providing a foundation for proactive disease management and early intervention strategies.