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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
Summary
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.
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.
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