TITLE:
Graph Neural Networks for Spatio-Temporal Forecasting of Foot-and-Mouth Disease Risk Using Livestock Movement Traceability Data
AUTHORS:
Samuel Ocen, Ritah Nafuna, Azizi Wasike
KEYWORDS:
Graph Neural Networks, Temporal Graph Networks, Predictive Modeling, Digital Surveillance, Infectious Disease Forecasting, Spatio-Temporal Data, Livestock Movement, Foot-and-Mouth Disease
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.18 No.1,
January
15,
2026
ABSTRACT: Foot-and-Mouth Disease (FMD) remains a critical threat to global livestock industries, causing severe economic losses and trade restrictions. This paper proposes a novel application of Temporal Graph Networks (TGNs) to forecast FMD outbreak risk with a four-week horizon using livestock movement traceability data. By modeling complex, time-evolving relationships between farms and geographic regions, the TGN framework captures the dynamic spatio-temporal dependencies that govern disease spread. We evaluate our model against several benchmarks, including Logistic Regression, LSTM, static Graph Convolutional Networks (GCNs), and a GCN-LSTM hybrid. Our results demonstrate that the proposed TGN model achieves superior performance, with an F1-score of 0.89 and an AUC-ROC of 0.94, significantly outperforming all baseline approaches. The study highlights the potential of advanced graph-based deep learning to enhance veterinary epidemiological surveillance and enable proactive disease control strategies through early warning systems.