Translating big data into smart data for veterinary epidemiology

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish (US)
Article number110
JournalFrontiers in Veterinary Science
Volume4
Issue numberJUL
DOIs
StatePublished - Jul 17 2017

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animal health
epidemiology
Epidemiology
Health
artificial intelligence
Risk Management
Population

Keywords

  • Animal movement
  • Big data
  • Machine learning
  • Modeling and simulation
  • Surveillance

Cite this

Translating big data into smart data for veterinary epidemiology. / VanderWaal, Kimberly; Morrison, Robert B.; Neuhauser, Claudia; Vilalta, Carles; Perez, Andres M.

In: Frontiers in Veterinary Science, Vol. 4, No. JUL, 110, 17.07.2017.

Research output: Contribution to journalArticle

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