Brucellosis-free status in Zhijiang County, Hubei, is jeopardised by the continual inflow of live cattle from high-prevalence provinces. This study integrated participatory value-chain mapping with stochastic risk modelling to quantify where and how Brucella could be introduced.

Two focus-group discussions, 48 key-informant interviews, and 31 site visits delineated the cattle production-to-consumption network, characterising trade volumes, stakeholder practices, and biosecurity gaps. These data parameterised a scenario-tree model run in @Risk (50,000 Monte Carlo iterations), and a Latin-hypercube global sensitivity analysis ranked the influence of input variables.

Brokers dominated live-animal movements, sourcing ~80 % of imports and frequently bypassing origin-quarantine. The median probability that an individual imported feeder was Brucella-positive was 1.43 % (95 % CI: 0.68–2.18), yielding an annual probability >99.999 % that at least one infected animal enters Zhijiang in 95 % of simulations. Over 90 % of total risk stemmed from cattle introduced via (i) unregulated brokers or (ii) trading platforms that omitted origin-quarantine or relied on low-sensitivity pen-side tests. Origin-herd prevalence and the proportion of cattle handled by brokers were the most influential parameters.

Even under conservative assumptions, current movement controls cannot prevent brucellosis incursion. Policy priorities include enforcing origin-quarantine, accrediting brokers, instituting centralized post-entry isolation, and deploying high-specificity rapid diagnostics. By coupling qualitative value-chain insights with quantitative risk assessment, this framework offers a transferable template for evidence-based disease-freedom programmes and highlights critical intervention points for safeguarding public health and livestock economies.