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.
|Original language||English (US)|
|Journal||Frontiers in Veterinary Science|
|State||Published - Jul 17 2017|
Bibliographical noteFunding Information:
This work was funded in part by the University of Minnesota MnDrive program and the Swine Health Information Center.
© 2017 VanderWaal, Morrison, Neuhauser, Vilalta and Perez.
- Animal movement
- Big data
- Machine learning
- Modeling and simulation