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首页> 美国卫生研究院文献>Frontiers in Veterinary Science >Effective Network Size Predicted From Simulations of Pathogen Outbreaks Through Social Networks Provides a Novel Measure of Structure-Standardized Group Size
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Effective Network Size Predicted From Simulations of Pathogen Outbreaks Through Social Networks Provides a Novel Measure of Structure-Standardized Group Size

机译：通过社交网络对病原体暴发进行模拟所预测的有效网络规模为结构标准化的团体规模提供了一种新的衡量标准

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The transmission of infectious disease through a population is often modeled assuming that interactions occur randomly in groups, with all individuals potentially interacting with all other individuals at an equal rate. However, it is well known that pairs of individuals vary in their degree of contact. Here, we propose a measure to account for such heterogeneity: effective network size (ENS), which refers to the size of a maximally complete network (i.e., unstructured, where all individuals interact with all others equally) that corresponds to the outbreak characteristics of a given heterogeneous, structured network. We simulated susceptible-infected (SI) and susceptible-infected-recovered (SIR) models on maximally complete networks to produce idealized outbreak duration distributions for a disease on a network of a given size. We also simulated the transmission of these same diseases on random structured networks and then used the resulting outbreak duration distributions to predict the ENS for the group or population. We provide the methods to reproduce these analyses in a public R package, “enss.” Outbreak durations of simulations on randomly structured networks were more variable than those on complete networks, but tended to have similar mean durations of disease spread. We then applied our novel metric to empirical primate networks taken from the literature and compared the information represented by our ENSs to that by other established social network metrics. In AICc model comparison frameworks, group size and mean distance proved to be the metrics most consistently associated with ENS for SI simulations, while group size, centralization, and modularity were most consistently associated with ENS for SIR simulations. In all cases, ENS was shown to be associated with at least two other independent metrics, supporting its use as a novel metric. Overall, our study provides a proof of concept for simulation-based approaches toward constructing metrics of ENS, while also revealing the conditions under which this approach is most promising.
机译：传染病在整个人群中的传播通常是在假设相互作用是随机发生的情况下进行建模的，所有个体都可能以相同的比率与所有其他个体相互作用。但是，众所周知，成对的人的接触程度不同。在这里，我们提出了一种解决这种异质性的措施：有效网络规模（ENS），它是指最大传播网络的大小（即非结构化，其中所有个体都与其他所有人平等地互动），对应于该网络的爆发特征给定的异构结构化网络。我们在最大完整网络上模拟了易感感染（SI）和易感感染恢复（SIR）模型，以在给定规模的网络上针对疾病生成理想的爆发持续时间分布。我们还模拟了这些相同疾病在随机结构网络上的传播，然后使用由此产生的爆发持续时间分布来预测群体或人群的ENS。我们提供了在公共R包“ enss”中重现这些分析的方法。随机结构网络上模拟的爆发持续时间比完整网络上模拟的爆发持续时间变化更大，但往往具有相似的疾病传播平均持续时间。然后，我们将我们的新指标应用于从文献中获得的经验灵长类动物网络，并将我们的ENS代表的信息与其他已建立的社交网络指标进行了比较。在AICc模型比较框架中，对于SI模拟，组大小和平均距离被证明是与ENS最一致的度量，而对于SIR模拟，组大小，集中化和模块化与ENS最一致。在所有情况下，已证明ENS与至少两个其他独立指标相关联，从而支持将其用作新指标。总体而言，我们的研究为基于模拟的方法建立ENS指标提供了概念验证，同时还揭示了该方法最有希望的条件。

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