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XGBoost-based early warning framework for farm-level PED risk using meteorological and herd management variables
Ran Guan1, Rui Xu2,3, Chaoyun Yang1
1College of Animal Science, Key Laboratory of Animal Epidemic Disease Detection and Prevention in Panxi District, Xichang University, Xichang 615013, China.
Iscience
|August 14, 2026
Summary
Porcine epidemic diarrhea (PED) outbreaks are influenced by weather and farm management. Pre-weaning mortality and seasonal factors are key predictors for PED risk, enabling better biosecurity.
Area of Science:
- Veterinary Epidemiology
- Animal Health Management
- Swine Production
Background:
- Porcine epidemic diarrhea (PED) causes significant economic losses in the global swine industry.
- PED outbreaks result from complex interactions between weather patterns and herd management.
- Predicting PED risk at the farm level is crucial for effective disease control.
Purpose of the Study:
- To develop a farm-level risk prediction framework for PED.
- To identify key meteorological and reproductive performance variables influencing PED outbreaks.
- To enhance PED surveillance and precision biosecurity management.
Main Methods:
- Integrated meteorological and reproductive performance data from southern China swine farms.
- Employed a variable selection strategy including hierarchical clustering, VIF filtering, and PCA.
- Utilized inverse-proportion class weighting to address class imbalance and XGBoost for classification.
Main Results:
- Reduced 22 meteorological predictors to 14 consensus variables.
- XGBoost demonstrated the most balanced predictive performance among five classifiers.
- Identified seasonal periodicity and pre-weaning mortality rate as primary PED risk determinants.
Conclusions:
- Multimodal farm-level data integration is vital for PED surveillance systems.
- Pre-weaning mortality is a significant indicator of PED risk.
- The study provides a quantitative foundation for precision biosecurity in swine farming.
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