Big Data and Precision Medicine

The increased availability and complexity of data has created new opportunities and problems in veterinary epidemiology, such as converting plentiful, diversified, and fast growing "big" data into valuable animal health insights. Big data analytics are used to identify high-risk populations, combine data or processes acting at multiple scales through epidemiological modelling approaches, and harness high velocity data to monitor animal health trends and detect emerging health threats in order to better understand health risks and minimize the impact of adverse animal health issues. While big data has been utilised in human medicine and public health to improve "precision" care and analyse trends in human diseases, big data in veterinary medicine has largely been employed for geographical analytics and bioinformatics. The use of big data for animal disease surveillance, on the other hand, is a rapidly emerging field.

Precision medicine has the potential to transform medical practice and enhance treatment outcomes. While the present focus is on human medicinal applications, the veterinary profession has plenty to offer and gain. Molecular phenotyping of animal diseases will link them to human disorders with similar phenotypes for which precise therapies have been found or are being developed. Beyond describing disease pathogenesis based on similar genetic processes between humans and animals, precision medicine promises benefits to veterinary clinical practice. Veterinary Precision Medicine (VPM) is described as an optimized preventive or curative therapeutic approach based on the identification of disease biomarkers and the application of disease monitoring technologies (right animal, right medicine, right dose, right time). Field veterinarians' roles may be altered in the future as a result of the development of such technology.

• Smart Data
• Machine Learning
• Artificial Intelligence
• Veterinary Oncology
• Precision Livestock Farming (Plf)
• Veterinary Precision Medicine (Vpm)