Predictive Machine Learning Models for Zoonotic Disease Surveillance: Implications for Animal Health and Veterinary Practice

Zoonotic diseases represent approximately 60-70% of new infectious diseases globally, resulting in yearly economic losses surpassing USD 120 billion from trade limitations, livestock deaths, and decreased productivity. Conventional veterinary surveillance systems, depending on manual reporting and lagging diagnostics, frequently identify outbreaks 10-14 days post-emergence, causing swift pathogen transmission. This research utilized predictive machine learning (ML) models on a synthesized dataset of 200,000 veterinary records that combined clinical, genomic, environmental, and climate factors. Supervised classifiers such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM, CatBoost), SVMs, and k-NN were assessed in both binary outbreak classification and multi-class risk prediction tasks. Random Forests attained the best AUC of 0.95, demonstrating 91% sensitivity and 88% specificity, cutting outbreak detection delay by 12 days relative to baseline reporting. Gradient Boosting models showed similar performance, achieving AUC values ranging from 0.93 to 0.94, especially standing out in structured surveillance information. Analysis of feature importance revealed that serology IgG (12.9%), antimicrobial resistance indicators (9.8%), and microbiome diversity metrics (8.5%) were the leading predictors, with climate factors accounting for another 7–10% of the variation in predicting vector-borne diseases. Case studies showed that ML models predicted avian influenza outbreaks in poultry with 92% accuracy, identified rabies in domestic and wild reservoirs with a 14% false positive rate, and forecasted brucellosis and bovine tuberculosis risks in cattle with a Cohen’s κ of 0.87, indicating strong alignment with expert assessments. The expansion of ticks and mosquitoes influenced by climate was forecasted with an average error of ±6.2% in three areas, highlighting the effectiveness of the models for proactive One Health monitoring. The results underscore how ML can improve veterinary diagnostic processes, enhance outbreak readiness, and bolster interdisciplinary One Health partnerships. In spite of issues related to data imbalance, generalizability, and restricted use in low-resource environments, predictive ML frameworks show significant promise in decreasing detection times by more than 70%, cut economic losses by USD 20-25 billion annually, and inform evidence-based veterinary policy.