Inferring influenza global transmission networks without complete phylogenetic information.
Inferring influenza global transmission networks without complete phylogenetic information.
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DOI：
10.1111/eva.12138
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发表时间：
2014-03
影响因子：
4.1
通讯作者：
Aris-Brosou S
中科院分区：
文献类型：
--
作者：
Aris-Brosou S
流感是影响世界各地人类的最严重的呼吸道感染之一，但其全球传播网络的动态仍然存在争议。在这里，我描述了一种新的系统发育学、时间序列和图论的组合来分析14.25年 年在空间和时间上分层的数据，重点是人类免疫反应的主要目标血凝素基因。在绕过庞大数据集的完整系统发育推断的同时，该方法仍然提取了表明遗传或核苷酸多样性的浪潮在全球持续传播的信息，这些亚型经历了几个季节的持续传播，例如H3N1和H1N1/09大流行，而流行前H1N1病毒的多样性在2009年之前一直具有与源/汇模型一致的非连续传播模式。不管随着H1N1/09大流行毒株的出现，H1N1多样性循环的结构发生了变化，美国的流行率在基因多样性处于最低水平的冬季月份达到顶峰。这表明，优势菌株通常是流行病的罪魁祸首，实时监测遗传和/或核苷酸多样性可以为公共卫生机构提供对流行率的间接估计。
Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25 years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence.
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影响因子：
3.4
作者：
Abdussamad J;Aris-Brosou S
通讯作者：
Aris-Brosou S
DOI：
10.1093/bioinformatics/btp348
发表时间：
2009-08-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Capella-Gutiérrez S;Silla-Martínez JM;Gabaldón T
通讯作者：
Gabaldón T
影响因子：
4.3
作者：
Lemey P;Rambaut A;Drummond AJ;Suchard MA
通讯作者：
Suchard MA
影响因子：
4.5
作者：
Murrell B;Wertheim JO;Moola S;Weighill T;Scheffler K;Kosakovsky Pond SL
通讯作者：
Kosakovsky Pond SL
影响因子：
3.7
作者：
Creanza N;Schwarz JS;Cohen JE
通讯作者：
Cohen JE