Epidemiologically optimal static networks from temporal network data.
Epidemiologically optimal static networks from temporal network data.
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DOI：
10.1371/journal.pcbi.1003142
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发表时间：
2013
影响因子：
4.3
通讯作者：
Holme P
中科院分区：
文献类型：
--
作者：
Holme P
网络流行病学的核心假设之一是传染病传播的接触结构可以表示为静态网络。然而，接触是高度动态的，在许多时间尺度上变化。在本文中，我们研究概念上简单的方法来构建静态图的网络流行病学的时间接触数据。我们评估这些方法的经验和合成模型数据。对于我们几乎所有的案例，捕捉最相关信息的网络表示是所谓的指数阈值网络。在这些中，每个接触都贡献了随时间呈指数下降的权重，并且如果它们之间的权重超过阈值，则在一对顶点之间存在边。在一个最佳选择的时间窗口上聚集的联系人网络的表现几乎与指数阈值网络一样好。另一方面，在整个采样时间内积累联系的网络，以及并发伙伴关系的网络，表现更差。我们讨论这些意见的时间和拓扑结构的数据集的上下文中。要了解疾病是如何在人群中传播的，研究接触人群的网络是很重要的。许多流行病爆发的建模方法都假设可以将该网络视为静态网络。在现实中，我们知道人与人之间的接触模式会随着时间而改变，旧的接触很快就不相关了--我们认识玛丽安托瓦内特的情人了解艾滋病流行并不重要。本文研究了构建尽可能与疾病传播相关的人群网络的方法。我们称之为指数阈值网络的最有前途的方法是让接触者的贡献越少，距离爆发的开始越远。我们研究的方法都在人工模型的接触模式和经验数据。除了寻找最佳的网络表示，我们还研究了原始数据集的结构如何影响表示的性能。
One of network epidemiology's central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets. To understand how diseases spread in a population, it is important to study the network of people in contact. Many methods to model epidemic outbreaks make the assumption that one can treat this network as static. In reality, we know that contact patterns between people change in time, and old contacts are soon irrelevant—it does not matter that we know Marie Antoinette's lovers to understand the HIV epidemic. This paper investigates methods for constructing networks of people that are as relevant as possible for disease spreading. The most promising method we call exponential-threshold network works by letting contacts contribute less, the further from the beginning of an outbreak they take place. We investigate the methods both on artificial models of the contact patterns and empirical data. Except searching for the optimal network representation, we also investigate how the structure of the original data set affects the performance of the representations.
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DOI：
10.12703/p5-6
发表时间：
2013
期刊：
F1000prime reports
影响因子：
--
作者：
Masuda N;Holme P
通讯作者：
Holme P
影响因子：
2.4
作者：
Ebel, H;Mielsch, LI;Bornholdt, S
通讯作者：
Bornholdt, S
影响因子：
3.1
作者：
Holme, P;Edling, CR;Liljeros, F
通讯作者：
Liljeros, F
影响因子：
2.4
作者：
Liu, Su-Yu;Baronchelli, Andrea;Perra, Nicola
通讯作者：
Perra, Nicola
DOI：
10.1073/pnas.0405728101
发表时间：
2004-10-05
影响因子：
11.1
作者：
Eckmann, JP;Moses, E;Sergi, D
通讯作者：
Sergi, D