Dynamics and control of diseases in networks with community structure.
Dynamics and control of diseases in networks with community structure.
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DOI：
10.1371/journal.pcbi.1000736
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发表时间：
2010-04-08
影响因子：
4.3
通讯作者：
Jones JH
中科院分区：
文献类型：
--
作者：
Salathé M;Jones JH
通过直接人际传播(如流感、天花、艾滋病毒/艾滋病等)传播传染病的动态。取决于底层主机联系网络。人际交往网络显示出强大的社区结构。了解这种社区结构如何影响流行病，可能有助于通过药物或非药物干预改变接触网络结构，从而防止疾病在社区之间传播。我们使用经验网络和模拟网络来研究疾病在具有社区结构的网络中的传播。我们发现社区结构对疾病动态有重要影响，并且我们表明，在具有较强社区结构的网络中，针对连接社区的个体的免疫干预比单纯针对高联系的个体的免疫干预更有效。由于相关接触网络的结构一般不为人所知，而且疫苗供应往往有限，因此非常需要高效的疫苗接种算法，而不需要完全了解网络。我们开发了一种算法，该算法只作用于本地可用的网络信息，并能够快速确定成功免疫干预的目标。在疫苗供应有限的情况下，特别是在具有较强社区结构的网络中，该算法的性能通常优于现有算法。了解传染病的传播并设计最佳控制策略是公共卫生的一个主要目标。社会网络显示出显著的社区结构模式，我们基于经验和模拟数据的结果表明，社区结构强烈影响疾病动态。这些结果对控制策略的设计具有一定的指导意义。了解传染病在人群中的传播是控制它们的关键。流行病的计算机模拟为研究流行病的动态提供了一个有价值的工具。在这种模拟中，种群由网络表示，其中主机及其相互之间的交互由节点和边表示。在过去的几年里，很明显，许多人类社交网络都有一个非常显著的属性：它们都表现出强大的社区结构。具有强大社区结构的网络由较小的子网络(社区)组成，这些子网络内部有许多连接，但它们之间只有很少的连接。在这里，我们使用来自社交网站和计算机生成网络的数据来研究社区结构对流行病传播的影响。我们发现，社区结构不仅影响网络中流行病的动态，而且还影响到如何保护网络免受大规模流行病的影响。
The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies. Understanding the spread of infectious diseases in populations is key to controlling them. Computational simulations of epidemics provide a valuable tool for the study of the dynamics of epidemics. In such simulations, populations are represented by networks, where hosts and their interactions among each other are represented by nodes and edges. In the past few years, it has become clear that many human social networks have a very remarkable property: they all exhibit strong community structure. A network with strong community structure consists of smaller sub-networks (the communities) that have many connections within them, but only few between them. Here we use both data from social networking websites and computer generated networks to study the effect of community structure on epidemic spread. We find that community structure not only affects the dynamics of epidemics in networks, but that it also has implications for how networks can be protected from large-scale epidemics.
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DOI：
10.1073/pnas.0706849105
发表时间：
2008-03-25
影响因子：
11.1
作者：
Halloran, M. Elizabeth;Ferguson, Neil M.;Cooley, Philip
通讯作者：
Cooley, Philip
DOI：
10.1098/rspb.1997.0131
发表时间：
1997-07-22
影响因子：
4.7
作者：
Edmunds, WJ;OCallaghan, CJ;Nokes, DJ
通讯作者：
Nokes, DJ
影响因子：
1.4
作者：
Eames, K. T. D.
通讯作者：
Eames, K. T. D.
DOI：
10.1209/epl/i2004-10550-5
发表时间：
2005-10-01
期刊：
EUROPHYSICS LETTERS
影响因子：
--
作者：
Liu, ZH;Hu, BB
通讯作者：
Hu, BB
影响因子：
1.9
作者：
ADDY, CL;LONGINI, IM;HABER, M
通讯作者：
HABER, M