Controlling enzootic livestock diseases is a major challenge for sustainable farming systems and veterinary public health. Because the diversity of farming systems and between-herd contacts influence pathogen spread, concerted large-scale interventions are needed. Particular attention should be paid to animal trade movements forming complex networks linking farms, which represent a major transmission pathway. Our objective is to provide a modelling framework for prioritizing regional control strategies of cattle enzootics. We developed data-driven multiscale stochastic epidemiological models describing detailed within-herd demography and infection dynamics coupled through between-herd contacts. This framework was applied to paratuberculosis (PTb; spread through trade), and bovine viral diarrhoea (BVD; spread through trade and neighbouring contacts). We modelled 12,857 dairy herds located in Brittany (France), using comprehensive datasets (2005-2013) on herd size, location, demography, and trade. Simulated control strategies implemented at different intensity levels combined biosecurity (test-&-cull, hygiene) and tests at purchase in all or targeted herds. According to our findings, only high intensity of measure implementation enabled to limit the spread of PTb at within and between-herd scales. For BVD, systematic tests at purchase largely reduced prevalence, but within-herd control was needed to reach eradication. Our study highlights the key challenge of controlling cattle enzootics, as a balance between efficacy and effort. In the front of multiple criteria to optimize, we provided a flexible and efficient tool to help animal health managers in defining relevant regional control strategies, accounting for specificities of the contact network and farm characteristics