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 Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

135
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
135
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K

You might also read

Related Articles

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

Sort by
Same author

Correction: Bayesian network models to assess antimicrobial resistance patterns of Streptococcus suis isolated from swine production systems in the United States between 2014-2021.

PLoS computational biology·2026
Same author

Tracking pathogen-related markers with eDNA in natural areas: how environmental factors shape surveillance strategies.

Veterinary research·2026
Same author

Assessment of Potential Zoonotic Risks in Aquaponic Lettuce Production: A Prototype for Experimental Greenhouse Trials.

Foodborne pathogens and disease·2026
Same author

Bayesian network models to assess antimicrobial resistance patterns of Streptococcus suis isolated from swine production systems in the United States between 2014-2021.

PLoS computational biology·2026
Same author

Spatiotemporal trends of pelvic organ prolapse incidence in North American swine breeding herds and association with climatic factors.

Frontiers in veterinary science·2026
Same author

Descriptive epidemiology of canine and feline cancer in California, United States from 2000 to 2019.

Veterinary journal (London, England : 1997)·2026
Same journal

Therapeutic potential of crude protein extracts from two Egyptian freshwater snails Lanistes carinatus and Bellamya unicolor.

Scientific reports·2026
Same journal

Microbial contamination of donor corneas and post-keratoplasty endophthalmitis: a comparison between Japanese and U.S. eye banks using cold storage.

Scientific reports·2026
Same journal

Prevalence and contributing factors of virological non-suppression among adult patients on first-line antiretroviral therapy in tertiary hospitals in Ethiopia.

Scientific reports·2026
Same journal

An in vitro comparison of color stability between alkasite and different restorative materials in various staining solutions.

Scientific reports·2026
Same journal

Toward accessible mRNA LNP formulation: systematic evaluation of mixing strategies and key parameters.

Scientific reports·2026
Same journal

A network analysis of personality traits, mentalizing, and psychological health in Chinese college students.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jul 13, 2025

Nasal Wipes for Influenza A Virus Detection and Isolation from Swine
05:59

Nasal Wipes for Influenza A Virus Detection and Isolation from Swine

Published on: December 4, 2015

9.5K

Infection prediction in swine populations with machine learning.

Avishai Halev1, Beatriz Martínez-López2, Maria Clavijo3

  • 1Department of Mathematics, University of California, Davis, Davis, CA, USA.

Scientific Reports
|October 18, 2023
PubMed
Summary
This summary is machine-generated.

A new machine learning model predicts swine infections days in advance, using farm density and piglet data. This early warning system aids disease prevention in the pork industry.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Related Experiment Videos

Last Updated: Jul 13, 2025

Nasal Wipes for Influenza A Virus Detection and Isolation from Swine
05:59

Nasal Wipes for Influenza A Virus Detection and Isolation from Swine

Published on: December 4, 2015

9.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Area of Science:

  • Animal Science
  • Veterinary Medicine
  • Data Science

Background:

  • Swine diseases significantly impact pork industry productivity and animal welfare, causing substantial economic losses.
  • Early detection of disease outbreaks is crucial for effective prevention and mitigation strategies in pig farming.

Purpose of the Study:

  • To develop and evaluate a machine learning model for daily prediction of swine infection emergence.
  • To identify key predictive features for early swine disease outbreak detection.
  • To assess the model's generalizability and performance across different swine production systems.

Main Methods:

  • Utilized machine learning to predict daily infection emergence in swine production systems.
  • Identified key predictors: nearby farm density, historical test rates, piglet inventory, gestation feed consumption, wind speed, and direction.
  • Evaluated model performance for 7- and 30-day advance outbreak prediction on two distinct swine production systems.

Main Results:

  • The model demonstrated good predictive ability for swine infections, with balanced accuracies up to [Formula: see text] for general diseases and specific pathogens like PRRSV, PEDV, Influenza A, and Mycoplasma hyopneumoniae.
  • Identified key features contributing to accurate infection prediction.
  • Analyzed the impact of data availability and granularity on model performance in different production settings.

Conclusions:

  • The developed machine learning model provides daily infection probabilities, serving as a valuable tool for veterinarians and stakeholders.
  • Enables timely support for preventive and control strategies, enhancing disease management in swine production.
  • Highlights the potential of data-driven approaches for improving animal health and productivity in the pork industry.