Advancing veterinary epidemiology by integration of machine learning: Current status and future perspectives

Abstract

The integration of machine learning (ML) in veterinary epidemiology offers transformative potential for data analysis and disease management, a significant shift from traditional statistical methods. This review explores the burgeoning role of ML, emphasizing its capacity to handle complex, high-dimensional data and uncover nonlinear relationships, which are pivotal in epidemiology. Key ML methodologies, including supervised, unsupervised, and reinforcement learning, provide robust frameworks for predictive modeling, pattern recognition, and decision-making processes. Applications in veterinary medicine are already evident in diagnostic imaging and animal behavior monitoring, showcasing ML's ability to enhance diagnostic accuracy and welfare monitoring.
Despite these advancements, the field faces challenges such as imbalanced datasets, data quality issues, and the need for interdisciplinary collaboration. Strategies like Synthetic Minority Over-sampling Technique and ensemble methods help address class imbalance, while robust preprocessing techniques mitigate data noise. Future advancements in natural language processing and reinforced learning promise further integration, optimizing disease surveillance and intervention strategies.
The review highlights the transformative potential of ML in veterinary epidemiology, advocating for continued research and collaboration to overcome existing hurdles. By leveraging ML's capabilities, veterinary professionals can improve disease prediction, develop targeted preventive programs, and enhance overall animal health and food security, marking a significant advancement in veterinary science.