Effects of heterogeneous and clustered contact patterns on infectious disease dynamics.
Effects of heterogeneous and clustered contact patterns on infectious disease dynamics.
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DOI：
10.1371/journal.pcbi.1002042
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发表时间：
2011-06
影响因子：
4.3
通讯作者：
Ancel Meyers L
中科院分区：
文献类型：
--
作者：
Volz EM;Miller JC;Galvani A;Ancel Meyers L
传染病的传播从根本上取决于人与人之间的接触模式。尽管接触网络的研究表明，接触次数和接触持续时间的异质性可能会产生深远的流行病学后果，但模型通常假设接触是随机选择的，从而忽略了接触的社会学、时间和/或空间聚类。在这里，我们研究异质和聚集性接触模式对流行病动态的同时影响。为了对群体结构进行建模，我们概括了具有可调度分布（每个节点的联系人数量）和聚类级别（三个派系的数量）的配置模型。为了对此类随机图的流行病动态进行建模，我们推导了一个易于处理的低维常微分方程组，该系统解释了网络结构对流行病过程的影响。我们发现聚类和度分布之间的相互作用是复杂的。聚类总是会减缓流行病的速度，但同时增加聚类和程度分布的方差可以增加最终的流行病规模。我们还表明，如果错误地假设传染期是同质的，基于键渗流的近似可能会存在很大偏差，并且这种偏差的大小随着网络中聚类数量的增加而增加。我们应用这种方法来模拟家庭内联系人的高度聚集，使用从社会互动调查数据估计的接触参数，并确定不考虑家庭结构的网络模型将出现偏差的条件。传染病的传播动态对易感者和感染者之间的相互作用模式敏感。众所周知，人类的社会接触是高度异质的（社会接触的数量从很少到很多）并且是高度聚集的（单个个体的社会接触也往往相互接触）。为了预测这些模式对传染病传播的影响，流行病学家已经开始使用随机网络模型，其中节点代表易感者、传染者或康复者，链接代表足以传播疾病的接触者。本文介绍了一种通用的数学模型，该模型同时考虑了异质连通性和聚类，并用它来量化聚类接触对流行病的相对影响，以及忽略传染期聚类和变异性时可能出现的预测偏差。
The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased. The transmission dynamics of infectious diseases are sensitive to the patterns of interactions among susceptible and infectious individuals. Human social contacts are known to be highly heterogeneous (the number of social contacts ranges from few to very many) and to be highly clustered (the social contacts of a single individual tend also to contact each other). To predict the impacts of these patterns on infectious disease transmission, epidemiologists have begun to use random network models, in which nodes represent susceptible, infectious, or recovered individuals and links represent contacts sufficient for disease transmission. This paper introduces a versatile mathematical model that takes both heterogeneous connectivity and clustering into account and uses it to quantify the relative impact of clustered contacts on epidemics and the prediction biases that can arise when clustering and variability in infectious periods are ignored.
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