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Development of a causal machine learning model for the diagnosis of African swine fever
Author(s)
Lububu, Steven
Date Issued
2025
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
This study investigates the causal relationship between African swine fever (ASF) viral load
and disease severity in domestic and wild pigs using machine learning models. A causality
model with linear regression and random forest regressor was developed to analyse ASF
transmission dynamics and symptom severity. The linear regression model achieved an R²
value of 83.68% with an MAE of 1.27, while the random forest model achieved an R² value of
58.10% with an MAE of 1.52, confirming strong predictive performance. The results highlight
the effectiveness of biosecurity, surveillance and culling measures in containing ASF and em phasize evidence-based policy making for disease control. This study provides actionable in sights for veterinarians, farmers and policy makers, contributing to ASF risk management and
prevention strategies. Future research should integrate AI-driven real-time surveillance and
genetic analysis to improve ASF outbreak prediction and global containment measures.
and disease severity in domestic and wild pigs using machine learning models. A causality
model with linear regression and random forest regressor was developed to analyse ASF
transmission dynamics and symptom severity. The linear regression model achieved an R²
value of 83.68% with an MAE of 1.27, while the random forest model achieved an R² value of
58.10% with an MAE of 1.52, confirming strong predictive performance. The results highlight
the effectiveness of biosecurity, surveillance and culling measures in containing ASF and em phasize evidence-based policy making for disease control. This study provides actionable in sights for veterinarians, farmers and policy makers, contributing to ASF risk management and
prevention strategies. Future research should integrate AI-driven real-time surveillance and
genetic analysis to improve ASF outbreak prediction and global containment measures.
Additional information
Thesis (Master of Information and Communication Technology)--Cape Peninsula University of Technology, 2025
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