Simulation models based on weighted multipartite animal trade networks for the optimized prediction and control of the transmission of classical swine fever

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In recent years, network analysis has become a valuable framework for the characterisation of animal trade networks. Here, the farms represent the nodes of the network, whereas the trade contacts between the single farms are the edges. Besides these monopartite networks (i.e. only one type of nodes), other possible disease transmission paths can be included in the analysis with so-called multipartite networks (e.g. group transports, feed supply). Moreover, edge weights (e.g. geographical distance, number of transported animals) are included in the network analysis. Therewith, it can be validated whether a more accurate prediction of the risk for disease transmission is given.Based on the edge weights and the various node types different network variations are built and compared by a sensitivity analysis. Thereby, the main influencing variables on the outcome of the network analysis can be identified. Thereby, the analyses of the original research Project are continued and open research questions can be comprehensively addressed.Due to the fact that recent studies showed that the majority of the parameters or algorithms are currently only available for undirected networks (i.e. the edge direction is neglected), a follow-up aim of this research project is, thus, to adapt these parameters to directed multipartite networks. Especially for animal trade networks and with this also in the case of the trade network of the ZNVG (Vermarktungsgesellschaft für Zucht- und Nutzvieh eG) Schleswig-Holstein the edge direction is important for the prediction of disease transmission and the implementation of appropriate control measures. Therewith, essential information which is provided by the pig trade network can be included in the analysis.In the next step, simulation models based on these weighted multipartite networks are established which allow the prediction of disease transmission and the implementation of control strategies for the classical swine fever virus. The integration of the versatile transmission paths as well as edge weights results in a realistic image of the disease transmission which is then compared to the outcome of the network analysis. Thus, the present simulation models differ from the classical simulation models. Here, only random connections between the nodes are intended. Furthermore, in the present simulation models, different control measures based on EU-legislative as well as on network and centrality parameters are implemented and their efficiency is verified.The comparison of the results from the simulation study and the weighted multipartite network analysis allows the determination of an appropriate and reliable data basis for an optimized prediction of disease transmission and provides insights in the development of suitable control strategies in the case of an epidemic. Thus, it becomes possible to decompose the underlying trade network and to interrupt the chain of infection.