Disease incursion and transmission modelling can play an important role in elucidating important pathways and dynamics of transboundary diseases. It is an important pre-requisite for preparedness and rapid response. A model framework has been developed which makes use of global datasets to predict the probability of entry of exotic animal pathogens to European Union (EU) member states (MSs) via some of the most likely routes of introduction: legal trade of livestock and meat products, illegal trade of red meat, wild animal dispersion, windborne vector dispersion and human introduction of pets. The model was designed to be applicable for a wide range of pathogens, many of which have limited data. We demonstrate its application through four case study pathogens: African swine fever, Classical swine fever, Bluetongue and classical rabies.

The model results highlight the differences in probability between EU MSs; the absolute values for entry via a given route differed across MSs whilst different pathogens were predicted as having the highest probability of entry for the same route across MSs. Scenario analyses suggested that the probability of entry was heavily influenced by the pathogen prevalence in the country of origin and the extent to which EU MSs pose a risk to each other; the greatest risk was predominantly from countries within the EU. While we believe the input data are obtained from high quality sources, there are still big issues with regards uncertainty in some areas, in particular with regards to prevalence of pathogens in vector populations and consistency of reporting of pathogen prevalence in animals across all countries of the world. Thus, it is inevitable that there is a high degree of uncertainty associated with the absolute values. However, the main strength of the model is the broad range of analyses over pathogens, EU MSs and routes of entry. The model is also relatively easy to update with new data and a web based visualisation tool has been developed which allows users to interrogate the results of the model. As such, we believe that the model proposed here can be a useful quantitative complement to current qualitative early warning systems, helping to drive risk-based surveillance activities, by providing detailed quantitative comparisons to indicate which pathogens are most likely to enter the EU, by which route and into which areas within Europe.

疾病入侵和传播模型可以在阐明跨界疾病的重要途径和动力学方面发挥重要作用。这是做好准备和迅速作出反应的重要先决条件。已经开发了一个模型框架，该模型框架利用全球数据集来预测外来动物病原体通过一些最可能的引入途径进入欧盟（EU）成员国的可能性：牲畜和肉类产品的合法贸易，红肉非法交易，野生动物散布，风媒传播和人类引入宠物。该模型旨在适用于多种病原体，其中许多病原体的数据有限。我们通过四个案例研究的病原体展示了其应用：非洲猪瘟，经典猪瘟，蓝舌病和经典狂犬病。

模型结果强调了欧盟成员国之间概率的差异；MS上通过给定路径进入的绝对值不同，而不同病原体被预测为MS上相同路径进入的可能性最高。方案分析表明，进入的可能性受到原产国病原体流行程度以及欧盟成员国之间相互威胁的程度的严重影响；最大的风险主要来自欧盟国家。尽管我们认为输入数据来自高质量来源，但在某些方面仍存在较大的不确定性，尤其是在媒介种群中病原体的流行以及该国所有国家动物中病原体流行的报告的一致性方面。世界。从而，不可避免地存在与绝对值相关的高度不确定性。但是，该模型的主要优点是可以对病原体，EU MS和进入途径进行广泛的分析。该模型还相对容易用新数据进行更新，并且已经开发了基于Web的可视化工具，该工具可让用户查询模型的结果。因此，我们认为此处提出的模型可以作为当前定性预警系统的有用的定量补充，通过提供详细的定量比较以表明哪些病原体最有可能进入欧盟，从而有助于推动基于风险的监视活动。欧洲的哪条路线和哪个区域。该模型的主要优点是可以对病原体，欧盟MS和进入途径进行广泛的分析。该模型还相对容易用新数据进行更新，并且已经开发了基于Web的可视化工具，该工具可让用户查询模型的结果。因此，我们认为此处提出的模型可以作为当前定性预警系统的有用的定量补充，通过提供详细的定量比较以表明哪些病原体最有可能进入欧盟，从而有助于推动基于风险的监视活动。欧洲的哪条路线和哪个区域。该模型的主要优点是可以对病原体，欧盟MS和进入途径进行广泛的分析。该模型还相对容易用新数据进行更新，并且已经开发了基于Web的可视化工具，该工具可让用户查询模型的结果。因此，我们认为此处提出的模型可以作为当前定性预警系统的有用的定量补充，通过提供详细的定量比较以表明哪些病原体最有可能进入欧盟，从而有助于推动基于风险的监视活动。欧洲的哪条路线和哪个区域。