Translating Big Data into Smart Data for Veterinary Epidemiology
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 m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-07-01
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| Series: | Frontiers in Veterinary Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fvets.2017.00110/full |
| _version_ | 1811259373931986944 |
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| author | Kimberly VanderWaal Robert B. Morrison Claudia Neuhauser Carles Vilalta Andres M. Perez |
| author_facet | Kimberly VanderWaal Robert B. Morrison Claudia Neuhauser Carles Vilalta Andres M. Perez |
| author_sort | Kimberly VanderWaal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T18:29:18Z |
| format | Article |
| id | doaj.art-9ef3723e472e480e866c4f6a9a6cb25f |
| institution | Directory Open Access Journal |
| issn | 2297-1769 |
| language | English |
| last_indexed | 2024-04-12T18:29:18Z |
| publishDate | 2017-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Veterinary Science |
| spelling | doaj.art-9ef3723e472e480e866c4f6a9a6cb25f2022-12-22T03:21:08ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692017-07-01410.3389/fvets.2017.00110244445Translating Big Data into Smart Data for Veterinary EpidemiologyKimberly VanderWaal0Robert B. Morrison1Claudia Neuhauser2Carles Vilalta3Andres M. Perez4Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United StatesDepartment of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United StatesInformatics Institute, University of Minnesota, Minneapolis, MN, United StatesDepartment of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United StatesDepartment of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, United StatesThe 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.http://journal.frontiersin.org/article/10.3389/fvets.2017.00110/fullanimal movementbig datamachine learningmodeling and simulationsurveillance |
| spellingShingle | Kimberly VanderWaal Robert B. Morrison Claudia Neuhauser Carles Vilalta Andres M. Perez Translating Big Data into Smart Data for Veterinary Epidemiology Frontiers in Veterinary Science animal movement big data machine learning modeling and simulation surveillance |
| title | Translating Big Data into Smart Data for Veterinary Epidemiology |
| title_full | Translating Big Data into Smart Data for Veterinary Epidemiology |
| title_fullStr | Translating Big Data into Smart Data for Veterinary Epidemiology |
| title_full_unstemmed | Translating Big Data into Smart Data for Veterinary Epidemiology |
| title_short | Translating Big Data into Smart Data for Veterinary Epidemiology |
| title_sort | translating big data into smart data for veterinary epidemiology |
| topic | animal movement big data machine learning modeling and simulation surveillance |
| url | http://journal.frontiersin.org/article/10.3389/fvets.2017.00110/full |
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