This study uses network analysis to evaluate how swine movements in Argentina could contribute to disease spread. Movement data for the 2014-2017 period were obtained from Argentina's online livestock traceability registry and categorized as follows: animals of high genetic value sent to other farms, animals to or from markets, animals sent to finisher operations and slaughterhouse. A network analysis was carried out considering the first three movement types. First, descriptive, centrality and cohesion measures were calculated for each movement type and year. Next, to determine whether networks had a small-world topology, these were compared with the results from random Erdös-Rényi network simulations. Then, the basic reproductive number (R0 ) of the genetic network, the group of farms with higher potential for disease spread standing at the top of the production chain, was calculated to identify farms acting as super-spreaders. Finally, their external biosecurity scores were evaluated. The genetic network in Argentina presented a scale-free and small-world topology. Thus, we estimate that disease spread would be fast, preferably to highly connected nodes and with little chances of being contained. Throughout the study, 31 farms were identified as super-spreaders in the genetic network for all years, while other 55 were super-spreaders at least once, from an average of 1,613 farms per year. Interestingly, removal of less than 5% of higher degree and betweenness farms resulted in a >90% reduction of R0 indicating that few farms have a key role in disease spread. When biosecurity scores of the most relevant super-spreaders were examined, it was evident that many were at risk of introducing and disseminating new pathogens across the whole of Argentina's pig production network. These results highlight the usefulness of establishing targeted surveillance and intervention programmes, emphasizing the need for better biosecurity scores in Argentinean swine production units, especially in super-spreader farms.

中文翻译：

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阿根廷养猪活动的网络分析：确定传染病传播中的关键农场及其生物安全水平。

这项研究使用网络分析来评估阿根廷的猪运动如何促进疾病传播。2014-2017年期间的运动数据是从阿根廷的在线牲畜可追溯性注册表中获得的，分类如下：将具有高遗传价值的动物送至其他农场，将动物送入市场或从市场运出，将动物送入肥育场和屠宰场。考虑前三种运动类型进行了网络分析。首先，针对每种运动类型和年份计算描述性，中心性和凝聚力度量。接下来，要确定网络是否具有小世界拓扑，请将其与随机Erdös-Rényi网络仿真的结果进行比较。然后，遗传网络的基本生殖数（R0），计算出位于生产链顶部的具有较高疾病传播潜力的农场群组，以识别充当超级传播者的农场。最后，评估了他们的外部生物安全性评分。阿根廷的遗传网络呈现出无标度的小世界拓扑。因此，我们估计疾病的传播速度会很快，最好传播到高度连接的节点，并且被传染的机会很小。在整个研究中，每年平均有1,613个农场中有31个农场被确定为遗传网络中的超级传播者，而其他55个农场至少一次是超级传播者。有趣的是，去除较高学位和中间性农场的比例不到5％，导致R0降低> 90％，这表明很少有农场在疾病传播中具有关键作用。当检查最相关的超级摊铺机的生物安全性分数时，很明显，许多人有在整个阿根廷养猪生产网络中引入和传播新病原体的风险。这些结果突出了建立有针对性的监测和干预计划的有用性，强调了阿根廷养猪生产单位，特别是超级撒播场中需要更高的生物安全评分。