  Assessing the risk of epidemic spread on networks and developing strategies
for its containment is of tremendous practical importance, both due to direct
effects in public health and its impact on economies. In this work we present
the numerical results of a stochastic, event-driven, hierarchical agent-based
model designed to reproduce the infectious dynamics of the cattle disease
called Bovine Viral Diarrhea (BVD), for which the corresponding network of
movements is the main route of spreading. For the farm-node dynamics, the model
takes into account a vast number of breeding, infectious and animal movement
mechanisms via a susceptible-infected-recovered (SIR) type of dynamics with an
additional permanently infectious class. The interaction between the farms is
described by a supply and demand farm manager mechanism governing the network
structure and dynamics. We discuss the disease and breeding dynamics, study
numerous mitigation strategies of present and past government regulations
taking Germany as a case-study country and perform a sensitivity analysis on
key parameters. We argue that the model, subject to calibration, has universal
predictive potential, can be extended to diseases beyond BVD and demonstrate
that appropriate measures can indeed lead to eradication regarding BVD. We
further present the results of the time-series of the model and conduct a
statistical analysis of and among the different mitigation strategies.
