Abstract
Foot-and-mouth disease (FMD) is a highly contagious viral disease affecting livestock, with significant economic impacts in East Africa. Livestock movements, driven by trade, transhumance, and cultural practices, are key drivers of FMD spread. This study employs network analysis to characterize livestock movement patterns and assess their role in FMD transmission across Kenya, Uganda, and Tanzania. We constructed a multi-species temporal network using movement data from 2018–2022, incorporating cattle, sheep, and goats. Network metrics (degree, betweenness, closeness centrality) identified high-risk nodes and routes. A stochastic SIR model simulated FMD spread under different movement scenarios. Results showed that 15% of nodes (markets and aggregation points) accounted for 70% of transmission events. Seasonal peaks in movements during dry seasons correlated with outbreak clusters. Targeted interventions at high-betweenness nodes reduced outbreak size by 45%. Our findings highlight the utility of network-based surveillance for FMD control in data-limited settings.