From Movement to Spread: Generating Livestock Contact Networks that Preserve Infection Dynamics
Animal trade links livestock holdings through contacts that change from day to day, making movement networks vital for epidemic analysis and control. Official movement records reveal these transmission routes, but privacy concerns often restrict access to the original data. Furthermore, epidemiological studies frequently require synthetic networks that accurately preserve the structural and temporal dynamics driving disease spread. Here we introduce NetForge, a mechanism-informed generative framework that learns recurring sender--receiver roles from movement and farm information of Dutch national pig-movement records. We compared generators with varying structural and temporal constraints. Among them, the Operational Stochastic Block Model regime performed best by combining learned trade partner structure with constraints on how contacts persist, return, or first appear. It closely matched the accumulation of potential spreading routes in the observed network and reproduced its simulated disease transmission trajectories. Together, we show that pairing trade structure with temporal constraints is essential for capturing infection dynamics. Because NetForge models trade data as a sequence of time-framed networks, it aligns well with routinely collected movement records and provides practical guidance for building more realistic synthetic movement networks from empirical trade records.