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Assessing the risk of epidemic spread on networks and developing strategiesfor its containment is of tremendous practical importance, both due to directeffects in public health and its impact on economies. In this work we presentthe numerical results of a stochastic, event-driven, hierarchical agent-basedmodel designed to reproduce the infectious dynamics of the cattle diseasecalled Bovine Viral Diarrhea (BVD), for which the corresponding network ofmovements is the main route of spreading. For the farm-node dynamics, the modeltakes into account a vast number of breeding, infectious and animal movementmechanisms via a susceptible-infected-recovered (SIR) type of dynamics with anadditional permanently infectious class. The interaction between the farms isdescribed by a supply and demand farm manager mechanism governing the networkstructure and dynamics. We discuss the disease and breeding dynamics, studynumerous mitigation strategies of present and past government regulationstaking Germany as a case-study country and perform a sensitivity analysis onkey parameters. We argue that the model, subject to calibration, has universalpredictive potential, can be extended to diseases beyond BVD and demonstratethat appropriate measures can indeed lead to eradication regarding BVD. Wefurther present the results of the time-series of the model and conduct astatistical analysis of and among the different mitigation strategies.

Containment strategies and statistical measures for the control of Bovine Viral Diarrhea spread in livestock trade networks