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本文引用的文献

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Second look at the spread of epidemics on networks.再探网络上的流行病传播
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Susceptible-infected-recovered epidemics in dynamic contact networks.动态接触网络中的易感-感染-康复流行病
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When individual behaviour matters: homogeneous and network models in epidemiology.当个体行为起作用时:流行病学中的同质模型和网络模型
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Epidemic dynamics on an adaptive network.自适应网络上的流行病动力学
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Predicting epidemics on directed contact networks.预测有向接触网络上的流行病。
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动态接触网络中的流行阈值。

Epidemic thresholds in dynamic contact networks.

作者信息

Volz Erik, Meyers Lauren Ancel

机构信息

Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA.

出版信息

J R Soc Interface. 2009 Mar 6;6(32):233-41. doi: 10.1098/rsif.2008.0218.

DOI:10.1098/rsif.2008.0218
PMID:18664429
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2659580/
Abstract

The reproductive ratio, R0, is a fundamental quantity in epidemiology, which determines the initial increase in an infectious disease in a susceptible host population. In most epidemic models, there is a specific value of R0, the epidemic threshold, above which epidemics are possible, but below which epidemics cannot occur. As the complexity of an epidemic model increases, so too does the difficulty of calculating epidemic thresholds. Here we derive the reproductive ratio and epidemic thresholds for susceptible-infected-recovered (SIR) epidemics in a simple class of dynamic random networks. As in most epidemiological models, R0 depends on two basic epidemic parameters, the transmission and recovery rates. We find that R0 also depends on social parameters, namely the degree distribution that describes heterogeneity in the numbers of concurrent contacts and the mixing parameter that gives the rate at which contacts are initiated and terminated. We show that social mixing fundamentally changes the epidemiological landscape and, consequently, that static network approximations of dynamic networks can be inadequate.

摘要

繁殖数R0是流行病学中的一个基本量,它决定了易感宿主群体中传染病的初始增长情况。在大多数流行病模型中,存在一个特定的R0值,即流行阈值,高于此值时流行病有可能发生,但低于此值时流行病则不会发生。随着流行病模型复杂性的增加,计算流行阈值的难度也会增加。在此,我们推导出了一类简单动态随机网络中易感-感染-康复(SIR)流行病的繁殖数和流行阈值。与大多数流行病学模型一样,R0取决于两个基本的流行病参数,即传播率和康复率。我们发现R0还取决于社会参数,即描述并发接触数量异质性的度分布以及给出接触开始和终止速率的混合参数。我们表明,社会混合从根本上改变了流行病学格局,因此,动态网络的静态网络近似可能并不充分。