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Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic.

利用航空运输数据进行系统地理重建及其在 2009 H1N1 甲型流感大流行中的应用。

  • 影响因子:4.35
  • DOI:10.1371/journal.pcbi.1007101
  • 作者列表:"Reimering S","Muñoz S","McHardy AC
  • 发表时间:2020-02-07
Abstract

:Influenza A viruses cause seasonal epidemics and occasional pandemics in the human population. While the worldwide circulation of seasonal influenza is at least partly understood, the exact migration patterns between countries, states or cities are not well studied. Here, we use the Sankoff algorithm for parsimonious phylogeographic reconstruction together with effective distances based on a worldwide air transportation network. By first simulating geographic spread and then phylogenetic trees and genetic sequences, we confirmed that reconstructions with effective distances inferred phylogeographic spread more accurately than reconstructions with geographic distances and Bayesian reconstructions with BEAST that do not use any distance information, and led to comparable results to the Bayesian reconstruction using distance information via a generalized linear model. Our method extends Bayesian methods that estimate rates from the data by using fine-grained locations like airports and inferring intermediate locations not observed among sampled isolates. When applied to sequence data of the pandemic H1N1 influenza A virus in 2009, our approach correctly inferred the origin and proposed airports mainly involved in the spread of the virus. In case of a novel outbreak, this approach allows to rapidly analyze sequence data and infer origin and spread routes to improve disease surveillance and control.

摘要

: 甲型流感病毒在人群中引起季节性流行和偶尔的大流行。虽然季节性流感的全球流行至少部分被理解,但国家、州或城市之间的确切迁移模式没有得到很好的研究。在这里,我们使用Sankoff算法与基于全球航空运输网络的有效距离一起进行简约的系统地理重建。通过首先模拟地理传播,然后是系统发育树和遗传序列,我们证实,与使用不使用任何距离信息的地理距离重建和使用BEAST的贝叶斯重建相比,使用有效距离推断的系统地理学重建传播更准确,并通过广义线性模型导致与使用距离信息的贝叶斯重建具有可比性的结果。我们的方法扩展了贝叶斯方法,通过使用机场等细粒度位置和推断抽样分离株中未观察到的中间位置,从数据中估计速率。当应用于 2009 年大流行H1N1 甲型流感病毒的序列数据时,我们的方法正确地推断了起源,并提出了主要参与病毒传播的机场。在新爆发的情况下,这种方法可以快速分析序列数据,推断起源和传播途径,以改善疾病监测和控制。

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翻译标题与摘要 下载文献
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DOI:10.1007/s15010-020-01401-y
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呼吸道感染方向

呼吸道感染分为上呼吸道感染与下呼吸道感染。上呼吸道感染是指自鼻腔至喉部之间的急性炎症的总称,是最常见的感染性疾病。下呼吸道感染是最常见的感染性疾患,治疗时必须明确引起感染的病原体以选择有效的抗生素。

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