Development of a causal machine learning model for the diagnosis of African swine fever

Ethics reference number is: 209002409/2023/11
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 emphasize evidence-based policy making for disease control. This study provides actionable insights 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.