Abstract
The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing “big” data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having “big data” to create “smart data,” with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.
Highlights
As our capacity to collect and store data continues to expand rapidly, challenges in veterinary epidemiology are shifting from data acquisition to translating data into meaningful insights about animal health
While human medicine and public health have harnessed big data to optimize “precision” care and track trends in human diseases [1,2,3,4,5,6,7,8], big data in the field of veterinary medicine have been mostly focused on spatial analyses and bioinformatics [9,10,11,12,13]
The development and refinement of such capabilities in veterinary epidemiology could significantly improve our ability to identify and respond to emerging animal health concerns, especially if collection and analysis of data occurs in near real-time rather than retrospectively
Summary
As our capacity to collect and store data continues to expand rapidly, challenges in veterinary epidemiology are shifting from data acquisition to translating data into meaningful insights about animal health. The development and refinement of such capabilities in veterinary epidemiology could significantly improve our ability to identify and respond to emerging animal health concerns, especially if collection and analysis of data occurs in near real-time rather than retrospectively.
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