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Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure

机译:接触网络结构对抗疫力传输树的影响:估算网络结构所需数据的影响

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Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute‐based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright ? 2017 John Wiley & Sons, Ltd.
机译:了解疾病的动态传播是发展有效干预控制或预防流行病的关键。已经显示出疾病传播的联系网络的结构对疫情的结果产生了强烈影响,但是一个开放的问题仍然是可以从流行病中收集的数据估计联系网络特征。本文采取的方法是检查具有特定结构特征的网络上产生的流行病结果的分布,以评估该结构是否可以从疫情数据测量,并且可能需要哪些其他限制来识别问题。为此,我们改变了病原体的网络大小,平均程度和传导性,以及感兴趣的网络特征:聚类,程度差异或基于属性的优先混合。我们录制了几种标准测量疫情的尺寸和传播,以及描述了传输树的形状的措施,以确定是否存在来自爆发的最终数据中的可检测信号。结果表明,潜力估计来自传输树或纯流行数据的接触网络特征,特别是对于具有高传输性的疾病或相关的接触网络具有低均值的疾病。版权? 2017年John Wiley& SONS,LTD.

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