Exotic animal diseases are transboundary hazards, characterized by their capability to cover global distances, affecting animal health and welfare with significant economic losses. Their prevention is complex and requires the dynamic management of potential entry points, transmission pathways, and preventative barriers. The well-timed detection of an undefined or unexpected (exotic or re-emerging) threat could minimize the consequences due to onward transmission. As a fit for purpose framework, OIE developed the import risk assessment i.e. a risk assessment model focusing on the entrance of an exotic disease into a geographical area with naïve hosts. In this paper, we propose an improvement of the model by integrating it with Social Network Analysis (SNA) accounting for within-country animal movements. Our integrated model has been used as a combined tool to better estimate the spatial probability of the introduction of at least one affected animal in Italian provinces using Bluetongue (BT) as an example. Starting from international country-specific BT prevalence data, the model estimated the probability of introduction to Italy via two different routes of release i.e. the import of infected animals or the release of infected vectors either associated with imported livestock or through windborne dispersion from Africa. The conventional OIE model estimating the probability of BT entering Italy assuming the same release probability for every Italian province was paralleled by a model integrated with outputs from SNA to account for different release probability among provinces based on animal movements. The conventional model predicted a remarkable homogeneity in the risk among the provinces with some peaks only visible during the warmest months. The model incorporating the network analysis predicted the highest risk to be in the North Eastern region of Italy but also highlighted the likely occurrence in a couple of Southern provinces, an output mirroring past occurrence of BT in Italy. Moreover, the sensitivity analysis highlighted the main role for a couple of model parameters i.e. the probability for a vector to become infected and the vaccine coverage, thus suggesting that an extra effort in vaccine campaigns could be envisaged. The ability to measure animal movements by SNA can allow the identification of geographical risk hot spots and therefore the risk-based targeting of the surveillance system.

外来动物疾病是跨界危害，其特征是它们能够跨越全球距离，影响动物健康和福利，并造成重大经济损失。它们的预防很复杂，需要动态管理潜在的切入点，传播途径和预防性障碍。及时发现未定义的或意外的（外来的或重新出现的）威胁可以将继续传播所造成的后果降至最低。作为适合目的的框架，世界动物卫生组织开发了进口风险评估，即一种风险评估模型，重点在于将外来疾病进入具有幼稚宿主的地理区域。在本文中，我们提出了一种模型的改进方法，将其与针对国家内部动物运动的社会网络分析（SNA）集成在一起。我们的综合模型已用作组合工具，以蓝舌（BT）为例，更好地估计了意大利各省引入至少一只受影响动物的空间概率。该模型从国际特定国家的BT流行率数据开始，通过两种不同的释放途径（即进口受感染动物或与进口牲畜有关或通过非洲通过风传播传播的受感染载体），估计了引入意大利的可能性。假设每个意大利省都有相同的释放概率，传统的OIE模型（估计BT进入意大利的概率）与集成了SNA输出的模型相平行，以解释基于动物运动的省份之间的不同释放概率。常规模型预测各省之间的风险具有显着的均一性，只有在最暖的月份才能看到一些高峰。包含网络分析的模型预测最高风险将发生在意大利的东北部地区，但同时也突显了南部几个省份可能发生的风险，其输出反映了意大利过去BT的发生。此外，敏感性分析强调了几个模型参数的主要作用，即载体被感染的可能性和疫苗覆盖率，因此表明可以设想在疫苗运动中付出额外的努力。通过SNA测量动物活动的能力可以识别地理风险热点，从而确定监视系统基于风险的目标。