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Kimberly VanderWaal1, Robert B Morrison1, Claudia Neuhauser2

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Summary
This summary is machine-generated.

Big data analytics offer new opportunities in veterinary epidemiology for understanding animal health risks and trends. Integrating new skills like machine learning is crucial for transforming big data into actionable insights for animal health management.

Keywords:
animal movementbig datamachine learningmodeling and simulationsurveillance

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Area of Science:

  • Veterinary Epidemiology
  • Data Science
  • Animal Health Management

Background:

  • The exponential growth of data presents both opportunities and challenges for veterinary epidemiology.
  • Translating large, diverse datasets into actionable animal health insights is a key concern.

Purpose of the Study:

  • To explore the application of big data analytics in veterinary epidemiology.
  • To highlight the need for new skills and approaches in training and practice.
  • To outline the progression from "big data" to "smart data" for improved animal health outcomes.

Main Methods:

  • Utilizing big data analytics for risk identification and trend monitoring.
  • Employing epidemiological modeling across multiple scales.
  • Harnessing high-velocity data for early detection of health threats.

Main Results:

  • Big data analytics can identify high-risk animal populations and emerging health threats.
  • Integration of machine learning and coding skills is essential for veterinary epidemiologists.
  • Real-time data analysis pipelines are key to creating "smart data".

Conclusions:

  • Big data analytics are transformative for veterinary epidemiology, enhancing understanding of animal health risks.
  • Adapting training programs to include data science skills is vital.
  • The development of "smart data" systems will improve animal health policy and disease prevention.