Development of a forecasting model for brucellosis spreading in the Italian cattle trade network aimed to prioritise the field interventions.

Brucellosis caused by Brucella abortus is an important zoonosis that constitutes a serious hazard to public health. Prevention of human brucellosis depends on the control of the disease in animals. Livestock movement data represent a valuable source of information to understand the pattern of contacts between holdings, which may determine the inter-herds and intra-herd spread of the disease. The manuscript addresses the use of computational epidemic models rooted in the knowledge of cattle trade network to assess the probabilities of brucellosis spread and to design control strategies. Three different spread network-based models were proposed: the DFC (Disease Flow Centrality) model based only on temporal cattle network structure and unrelated to the epidemiological disease parameters; a deterministic SIR (Susceptible-Infectious-Recovered) model; a stochastic SEIR (Susceptible-Exposed-Infectious-Recovered) model in which epidemiological and demographic within-farm aspects were also modelled. Containment strategies based on farms centrality in the cattle network were tested and discussed. All three models started from the identification of the entire sub-network originated from an infected farm, up to the fifth order of contacts. Their performances were based on data collected in Sicily in the framework of the national eradication plan of brucellosis in 2009. Results show that the proposed methods improves the efficacy and efficiency of the tracing activities in comparison to the procedure currently adopted by the veterinary services in the brucellosis control, in Italy. An overall assessment shows that the SIR model is the most suitable for the practical needs of the veterinary services, being the one with the highest sensitivity and the shortest computation time.