Phylodynamic analysis of a viral infection network.
Phylodynamic analysis of a viral infection network.
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DOI：
10.3389/fmicb.2012.00278
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发表时间：
2012
影响因子：
5.2
通讯作者：
Shiino T
中科院分区：
文献类型：
--
作者：
Shiino T
通过性传播和飞沫传播途径的病毒感染通常通过复杂的宿主-宿主接触网络传播。澄清影响特定病原体变化和动态的传播网络和流行病学参数是控制传染病的一个主要问题。然而，传统的方法，如采访和/或经典的病毒基因序列的系统发育分析有固有的局限性，往往无法检测到传染性集群和传播连接。计算环境的最新改进现在允许分析大型数据集。此外，新的分析方法已经开发出来，用于推断病毒遗传多样性的进化动力学使用样本数据信息和序列数据。这种类型的框架，被称为“病毒传播动力学”，有助于连接病毒传播网络上的一些缺失环节，这些环节通常很难通过传统的流行病学方法检测到。有了足够数量的序列，可以使用这种新的推断方法来估计理论流行病学参数，如时间分布的原发感染，病原体种群规模的波动，基本繁殖数，和疾病传染性的平均时间跨度。由该框架估计的传播网络通常具有无标度网络的性质，这是传染性和社会传播过程的特征。基于感染动力学的网络分析已经暗示了关于与给定社区和/或风险行为相关联的感染动力学的各种建议。在这篇综述中，我将总结目前的方法用于确定传输网络使用的无标度的属性，并提出了一个参数的可能性，这些现有的框架。
Viral infections by sexual and droplet transmission routes typically spread through a complex host-to-host contact network. Clarifying the transmission network and epidemiological parameters affecting the variations and dynamics of a specific pathogen is a major issue in the control of infectious diseases. However, conventional methods such as interview and/or classical phylogenetic analysis of viral gene sequences have inherent limitations and often fail to detect infectious clusters and transmission connections. Recent improvements in computational environments now permit the analysis of large datasets. In addition, novel analytical methods have been developed that serve to infer the evolutionary dynamics of virus genetic diversity using sample date information and sequence data. This type of framework, termed “phylodynamics,” helps connect some of the missing links on viral transmission networks, which are often hard to detect by conventional methods of epidemiology. With sufficient number of sequences available, one can use this new inference method to estimate theoretical epidemiological parameters such as temporal distributions of the primary infection, fluctuation of the pathogen population size, basic reproductive number, and the mean time span of disease infectiousness. Transmission networks estimated by this framework often have the properties of a scale-free network, which are characteristic of infectious and social communication processes. Network analysis based on phylodynamics has alluded to various suggestions concerning the infection dynamics associated with a given community and/or risk behavior. In this review, I will summarize the current methods available for identifying the transmission network using phylogeny, and present an argument on the possibilities of applying the scale-free properties to these existing frameworks.
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DOI：
10.1016/j.meegid.2011.08.005
发表时间：
2011-12
期刊：
Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
影响因子：
--
作者：
Kühnert D;Wu CH;Drummond AJ
通讯作者：
Drummond AJ
影响因子：
9.8
作者：
Drummond AJ;Ho SY;Phillips MJ;Rambaut A
通讯作者：
Rambaut A
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
56.9
作者：
Korber, B;Muldoon, M;Bhattacharya, T
通讯作者：
Bhattacharya, T
影响因子：
3.7
作者：
Bernini F;Ebranati E;De Maddalena C;Shkjezi R;Milazzo L;Lo Presti A;Ciccozzi M;Galli M;Zehender G
通讯作者：
Zehender G