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2014年塞拉利昂埃博拉病毒疫情的系统发育动力学分析。

Phylodynamic analysis of ebola virus in the 2014 sierra leone epidemic.

作者信息

Volz Erik, Pond Sergei

机构信息

Imperial College London.

University of California San Diego.

出版信息

PLoS Curr. 2014 Oct 24;6:ecurrents.outbreaks.6f7025f1271821d4c815385b08f5f80e. doi: 10.1371/currents.outbreaks.6f7025f1271821d4c815385b08f5f80e.

Abstract

BACKGROUND

The Ebola virus (EBOV) epidemic in Western Africa is the largest in recorded history and control efforts have so far failed to stem the rapid growth in the number of infections. Mathematical models serve a key role in estimating epidemic growth rates and the reproduction number (R0) from surveillance data and, recently, molecular sequence data. Phylodynamic analysis of existing EBOV time-stamped sequence data may provide independent estimates of the unobserved number of infections, reveal recent epidemiological history, and provide insight into selective pressures acting upon viral genes.

METHODS

We fit a series mathematical models of infectious disease dynamics to phylogenies estimated from 78 whole EBOV genomes collected from distinct patients in May and June of 2014 in Sierra Leone, and perform evolutionary analysis on these genomes combined with closely related EBOV genomes from previous outbreaks. Two analyses are conducted with values of the latent period that have been used in recent modelling efforts. We also examined the EBOV sequences for evidence of possible episodic adaptive molecular evolution during the 2014 outbreak.

RESULTS

We find evidence for adaptive evolution affecting L and GP protein coding regions of the EBOV genome, which is unlikely to bias molecular clock and phylodynamic analyses. We estimate R0=2.40 (95% HPD:1.54-3.87 ) if the mean latent period is 5.3 days, and R0=3.81, (95% HPD:2.47-6.3) if the mean latent period is 12.7 days. The estimated coefficient of variation (CV) of the number of transmissions per infected host is very high, and a large proportion of infections yield no transmissions.

CONCLUSIONS

Estimates of R0 are sensitive to the unknown latent infectious period which can not be reliably estimated from genetic data alone. EBOV phylogenies show significant evidence for superspreading and extreme variance in the number of transmissions per infected individual during the early epidemic in Sierra Leone.

摘要

背景

西非的埃博拉病毒(EBOV)疫情是有记录以来规模最大的,迄今为止的防控努力未能遏制感染人数的快速增长。数学模型在根据监测数据以及最近的分子序列数据估算疫情增长率和繁殖数(R0)方面发挥着关键作用。对现有的带有时间戳的EBOV序列数据进行系统发育动力学分析,可能会提供对未观察到的感染数量的独立估计,揭示近期的流行病学历史,并深入了解作用于病毒基因的选择压力。

方法

我们将一系列传染病动力学数学模型拟合到从2014年5月和6月在塞拉利昂不同患者身上收集的78个完整EBOV基因组估计的系统发育树上,并结合先前疫情中密切相关的EBOV基因组对这些基因组进行进化分析。使用近期建模工作中采用的潜伏期值进行了两项分析。我们还检查了EBOV序列,以寻找2014年疫情期间可能发生的间歇性适应性分子进化的证据。

结果

我们发现有证据表明适应性进化影响了EBOV基因组的L和GP蛋白编码区域,这不大可能使分子钟和系统发育动力学分析产生偏差。如果平均潜伏期为5.3天,我们估计R0 = 2.40(95%最高后验密度区间:1.54 - 3.87);如果平均潜伏期为12.7天,则R0 = 3.81(95%最高后验密度区间:2.47 - 6.3)。每个感染宿主的传播次数的估计变异系数(CV)非常高,并且很大一部分感染没有产生传播。

结论

R0的估计对未知的潜伏感染期很敏感,而仅从基因数据无法可靠地估计该潜伏期。EBOV系统发育树显示出在塞拉利昂疫情早期存在超级传播以及每个感染个体传播次数极端变异的显著证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7be6/4399230/69742e3f257d/formula0.jpg

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