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

近年来，网络分析已成为一个有价值的框架，动物贸易网络的特点。在这里，农场代表网络的节点，而单个农场之间的贸易联系是边。除了这些单部网络（即只有一种类型的节点），其他可能的疾病传播路径可以包括在所谓的多部网络（例如群体运输，饲料供应）的分析中。此外，边缘权重（例如地理距离、运输动物的数量）被包括在网络分析中。基于边权值和不同的节点类型，建立了不同的网络变量，并通过灵敏度分析进行了比较。因此，可以识别网络分析结果的主要影响变量。因此，原研究项目的分析是继续和开放的研究问题，可以全面解决。由于最近的研究表明，大多数参数或算法目前只适用于无向网络（即边缘方向被忽略），本研究项目的后续目标是，因此，这些参数适用于有向多部网络。特别是对于动物贸易网络以及石勒苏益格-荷尔斯泰因州ZNVG（Vermarttungsgesellschaft für Zucht- und Nutzvieh eG）贸易网络而言，边缘方向对于疾病传播的预测和适当控制措施的实施非常重要。在此基础上，建立了基于加权多部网络的模拟模型，为猪瘟病毒的传播预测和控制策略的实施提供了依据。通用的传播路径以及边缘权重的集成导致疾病传播的真实图像，然后将其与网络分析的结果进行比较。因此，目前的仿真模型不同于经典的仿真模型。在这里，仅意图节点之间的随机连接。此外，在本模拟模型中，根据欧盟的不同控制措施，通过比较模拟研究和加权多部网络分析的结果，可以确定一个适当和可靠的数据基础，用于疾病传播的优化预测，并为开发适当的在疫情发生时的控制策略。因此，可以分解底层贸易网络并中断感染链。

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.