Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Dec 23, 2025

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs
06:30

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Published on: June 5, 2019

8.8K

Determining pig holding type from British movement data using analytical and machine learning approaches.

R P Smith1, C Gavin1, D Gilson1

  • 1Animal and Plant Health Agency (APHA) - Weybridge, Woodham Lane, New Haw, Addlestone, Surrey, KT15 3NB, United Kingdom.

Preventive Veterinary Medicine
|April 18, 2020
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mitochondrial heteroplasmy-phenotype correlation and response to glucose lowering therapy in subjects with m.3243A>G mutations.

Diabetes & metabolism·2025
Same author

UK-wide risk factor study of broiler carcases highly contaminated with Campylobacter.

Zoonoses and public health·2023
Same author

An Evaluation Report on Radiotherapy Contouring Workshops During the Royal College of Radiologists' Annual Meeting 2018.

Clinical oncology (Royal College of Radiologists (Great Britain))·2019
Same author

Implementing the Royal College of Radiologists' Radiotherapy Target Volume Definition and Peer Review Guidelines: More Still To Do?

Clinical oncology (Royal College of Radiologists (Great Britain))·2019
Same author

Quality Assurance Peer Review for Radiotherapy for Haematological Malignancies.

Clinical oncology (Royal College of Radiologists (Great Britain))·2019
Same author

Target Volume Delineation Training for Clinical Oncology Trainees: The Role of ARENA and COPP.

Clinical oncology (Royal College of Radiologists (Great Britain))·2019

Accurately defining British pig population demographics is crucial for disease surveillance. Combining expert opinion, machine learning, and survey data offers the most precise method for classifying pig holdings and herd sizes.

Area of Science:

  • Veterinary epidemiology
  • Agricultural data science
  • Livestock population dynamics

Background:

  • Understanding livestock population structure is vital for effective disease surveillance and epidemiological research.
  • Accurate data on herd size, location, and demographics are essential for sample size calculations, cost-benefit analyses, and disease spread modeling.

Purpose of the Study:

  • To evaluate the suitability of British pig movement data for defining pig holding demographics.
  • To identify pig holding locations and estimate herd sizes using available datasets.
  • To compare the accuracy of epidemiological and machine learning methods for classifying pig holding types.

Main Methods:

  • Pig movement data was compared with other British pig population datasets to assess its appropriateness for demographic analysis.
Keywords:
AlgorthmHolding typeMachine-learningMovementPig

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

477
Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
07:52

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

14.9K

Related Experiment Videos

Last Updated: Dec 23, 2025

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs
06:30

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Published on: June 5, 2019

8.8K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

477
Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
07:52

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

14.9K
  • Two methods were employed to classify pig holdings: an epidemiological approach using expert-defined rules and a machine learning approach (k-means clustering).
  • Both methods were validated against data from the Annual June Agricultural Survey.

    • Main Results:

    • Both the epidemiological and machine learning methods demonstrated good accuracy in classifying pig holdings.
    • A consensus model, integrating both methods and survey data, yielded the most accurate classification of pig holdings.
    • The machine learning approach proved significantly faster and easier to update with new information compared to the epidemiological method.

    Conclusions:

    • A consensus model combining epidemiological rules, machine learning, and survey data provides the most accurate characterization of British pig holdings.
    • Machine learning offers a rapid and adaptable tool for livestock population analysis, despite requiring initial technical expertise.
    • Accurate pig population data is fundamental for robust disease control strategies and research initiatives.