传染病通过聚集的人群传播
Spread of infectious diseases through clustered populations
摘要：
人与人之间的接触网络构成了传染病传播的基础。大多数基于网络的传播研究都集中在程度（个人接触的数量）变化的影响上。然而，其他影响，如聚集性、传染性或易感性的变化或接触密切程度的变化可能起重要作用。我们开发了分析技术来预测这些影响如何改变流行病的增长率，概率和规模，并通过现实的社交网络验证预测。我们发现，（对于给定的度分布和平均传播率）聚类是控制增长率的主导因素，异质性的传染性是控制流行病的概率的主导因素，和异质性的易感性是控制流行病的规模的主导因素。边权重（测量接触的亲密度或持续时间）只有在不同边之间存在相关性时才有影响。综合起来，这些效应在相互加强方面可以发挥较小的作用，当人口异质性最大或如果更密切的接触者也强烈聚集时，聚集的影响最大。我们最重要的贡献是一个系统的方法来解决传染病模型中的聚类问题，我们的研究结果对干预措施的设计有许多影响。
英文摘要：
Networks of person-person contacts form the substrate along which infectious diseases spread. Most network-based studies of the spread focus on the impact of variations in degree (the number of contacts an individual has). However, other effects such as clustering, variations in infectiousness or susceptibility, or variations in closeness of contacts may play a significant role. We develop analytic techniques to predict how these effects alter the growth rate, probability, and size of epidemics and validate the predictions with a realistic social network. We find that (for given degree distribution and average transmissibility) clustering is the dominant factor controlling the growth rate, heterogeneity in infectiousness is the dominant factor controlling the probability of an epidemic, and heterogeneity in susceptibility is the dominant factor controlling the size of an epidemic. Edge weights (measuring closeness or duration of contacts) have impact only if correlations exist between different edges. Combined, these effects can play a minor role in reinforcing one another, with the impact of clustering largest when the population is maximally heterogeneous or if the closer contacts are also strongly clustered. Our most significant contribution is a systematic way to address clustering in infectious disease models, and our results have a number of implications for the design of interventions.
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