ArticleFrontiers in veterinary science2017
Translating Big Data into Smart Data for Veterinary Epidemiology.
Article in Frontiers in veterinary science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Simulating contact networks for livestock disease epidemiology: a systematic review.Journal of the Royal Society, Interface · 2023Pooled it
- Reusability challenges of livestock production data to improve animal health.Scientific data · 2025Article
- Developing a one health data integration framework focused on real-time pathogen surveillance and applied genomic epidemiology.One health outlook · 2025Article
- How do pig veterinarians view technology-assisted data utilisation for pig health and welfare management? A qualitative study in Spain, the Netherlands, and Ireland.Porcine health management · 2024Article
- Risk-Based Targeting of Animals for Ancillary Testing during a Bovine Tuberculosis Breakdown Is Associated with a Reduced Time to Test Failure: Indirect Evidence ofPathogens (Basel, Switzerland) · 2024Article
- Quantification of the effect of in utero events on lifetime resilience in dairy cows.Journal of dairy science · 2024Article
- Article
- Livestock and environmental characterization of Colombian municipalities: study of vesicular stomatitis.Frontiers in veterinary science · 2024Article
- When Everything Becomes Bigger: Big Data for Big Poultry Production.Animals : an open access journal from MDPI · 2023Review
- ASAS-NANP symposium: mathematical modeling in animal nutrition-Making sense of big data and machine learning: how open-source code can advance training of animal scientists.Journal of animal science · 2023Article
- ASAS-NANP symposium: mathematical modeling in animal nutrition: the progression of data analytics and artificial intelligence in support of sustainable development in animal science.Journal of animal science · 2022Article
- Australian veterinarians' perspectives on the contribution of the veterinary workforce to the Australian animal health surveillance system.Frontiers in veterinary science · 2022Article
- Connecting livestock disease dynamics to human learning and biosecurity decisions.Frontiers in veterinary science · 2022Article
- Man's best friend in life and death: scientific perspectives and challenges of dog brain banking.GeroScience · 2021Review
- Research perspectives on animal health in the era of artificial intelligence.Veterinary research · 2021Review
- One Digital Health: A Unified Framework for Future Health Ecosystems.Journal of medical Internet research · 2021Article
- Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil.Microorganisms · 2021Article
- Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making.Frontiers in veterinary science · 2021Article
- Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2020Article
- Near Real-Time Monitoring of Clinical Events Detected in Swine Herds in Northeastern Spain.Frontiers in veterinary science · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.