Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigation
实时跟踪病毒进化,用于疫苗株选择和流行病学调查
基本信息
- 批准号:10206776
- 负责人:
- 金额:$ 20.36万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-08-23 至 2022-03-31
- 项目状态:已结题
- 来源:
- 关键词:2019-nCoVAfricanAmericanCOVID-19 pandemicCommunicable DiseasesContact TracingDataData SetDengue VirusDisease OutbreaksEarly DiagnosisEbolaEbola virusEpidemicEpidemiologistEpidemiologyEvolutionFutureGenesGeneticGenomicsGeographyGoalsHumanImmunityInfluenzaMethodologyMethodsModelingMonitorMumps virusPatternPopulationProcessPublic HealthTimeTuberculosisUnited StatesUpdateVaccinesVariantViralViral GenomeViral Load resultVirusWorkZIKAZika Virusbioinformatics pipelineepidemiology studygenomic datagenomic epidemiologyimprovedinfluenza virus vaccineinfluenzavirusinnovationnovelpandemic diseasepathogenpathogenic viruspublic health interventionresponseseasonal influenzatooltransmission processvaccine efficacyviral genomicsweb site
项目摘要
Project Summary
Viral pathogens are an enduring threat to global public health. This project aims to use viral genomic data
to improve understanding of ongoing virus evolution and to make actionable inferences to reduce the global
burden of viral infectious disease. In order to be relevant for public health interventions, analyses of viral
sequence data need to be incredibly rapid, both in terms of computation and in terms of dissemination. To
accomplish these goals, this project will create novel methodological tools to analyze evolutionary dynamics
from influenza genetic sequence data and to analyze transmission patterns from outbreak sequence data.
Over the current project period (2016-2021), we developed a real-time analysis platform called Nextstrain,
which provides up-to-date analyses for a variety of pathogens including influenza virus, Ebola virus, Zika
virus, dengue virus, mumps virus, tuberculosis and SARS-CoV-2. Bioinformatic pipelines developed through
Nextstrain are reusable by academic groups and public health labs and resulting analyses are shareable via the
website nextstrain.org.
In the upcoming project period (2021-2026), we will refine methods for forecasting strain dynamics of influenza
virus. Monitoring and forecasting evolution of viral strains is of paramount importance. New antigenic variants
of influenza that partially escape from prior human immunity emerge and rapidly sweep through the viral
population. Such strains are less susceptible to vaccine-derived immunity and so antigenic evolution results in
the need to frequently update the seasonal influenza vaccine. This project aims to refine methods to forecast
strain dynamics and predict the makeup of the future influenza population. This forecasting is especially
relevant to influenza vaccine strain selection, as a vaccine strain is chosen for the Northern Hemisphere in
February for deployment the following winter. Accurate projections will aid in vaccine match for seasonal
influenza viruses and result in improved vaccine efficacy. Technical innovations focus on extending models to
work across different viruses, different gene segments and to incorporate spatial dynamics.
In an outbreak scenario such as the West African Ebola epidemic, the American Zika epidemic or the SARS-
CoV-2 pandemic, the focus of public health interventions focus on early diagnosis, contact tracing, isolation and
treatment. Epidemiological understanding of transmission dynamics is of paramount importance to outbreak
response. Viral genomic data can reveal otherwise hidden transmission patterns and aid in efficient contact
tracing. Geographic spread is especially amenable to genomic inferences. This project will develop tools to
make epidemiological inferences from outbreak sequence data. These methods will continue to be deployed
via the Nextstrain platform, allowing epidemiologists throughout the world to analyze their own datasets.
Genomic epidemiology has the potential to truly inform outbreak response. Nextstrain has been instrumental
to SARS-CoV-2 genomic epidemiology in the United States and world. Improvements to the accuracy and
capabilities of the platform would be well placed.
项目摘要
病毒病原体是对全球公共卫生的持久威胁。该项目旨在利用病毒基因组数据
提高对正在进行的病毒演变的理解,并做出可行的推断,以减少全球
病毒性传染病的负担。为了与公共卫生干预措施相关,
序列数据在计算和传播方面都需要令人难以置信的快速。到
为了实现这些目标,该项目将创建新的方法工具来分析进化动力学
并分析疫情序列数据的传播模式。
在当前项目期间(2016 - 2021年),我们开发了一个名为Nextstrain的实时分析平台,
它提供了各种病原体的最新分析,包括埃博拉病毒,寨卡病毒,
病毒、登革热病毒、腮腺炎病毒、结核病和SARS-CoV-2。生物信息学管道通过
Nextstrain可由学术团体和公共卫生实验室重复使用,结果分析可通过
网址:nextstrain.org。
在即将到来的项目期间(2021 - 2026年),我们将改进预测伊瓜苏应变动态的方法。
病毒监测和预测病毒株的演变至关重要。新的抗原变体
部分逃脱先前人类免疫力的病毒出现并迅速席卷病毒
人口这些菌株对疫苗衍生的免疫力较不敏感,因此抗原进化导致
需要经常更新季节性流感疫苗。该项目旨在改进预测方法,
应变动力学和预测未来的组成在Eschuenza人口。这一预测尤其
与流感疫苗株选择相关,因为选择了北方半球的疫苗株,
2月部署第二个冬天。准确的预测将有助于季节性疫苗匹配
从而提高疫苗的有效性。技术创新的重点是将模型扩展到
在不同的病毒,不同的基因片段中工作,并结合空间动力学。
在西非埃博拉疫情、美国寨卡疫情或SARS等疫情爆发的情况下,
在CoV-2大流行期间,公共卫生干预措施的重点是早期诊断、接触者追踪、隔离和
治疗对传播动力学的流行病学了解对暴发至关重要
反应病毒基因组数据可以揭示隐藏的传播模式,并有助于有效接触
追踪地理传播特别容易受到基因组推断的影响。该项目将开发工具,
根据爆发序列数据进行流行病学推断。这些方法将继续部署
通过Nextstrain平台,让世界各地的流行病学家分析他们自己的数据集。
基因组流行病学有可能真正为疫情应对提供信息。Nextstrain一直在帮助
SARS-CoV-2基因组流行病学在美国和世界。提高了准确性,
该平台的能力将得到很好的发挥。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Trevor BC Bedford其他文献
Trevor BC Bedford的其他文献
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{{ truncateString('Trevor BC Bedford', 18)}}的其他基金
Forecasting influenza evolution on a heterogeneous immune landscape
预测异质免疫环境中流感的演变
- 批准号:
10350150 - 财政年份:2022
- 资助金额:
$ 20.36万 - 项目类别:
Forecasting influenza evolution on a heterogeneous immune landscape
预测异质免疫环境中流感的演变
- 批准号:
10573200 - 财政年份:2022
- 资助金额:
$ 20.36万 - 项目类别:
Forecasting influenza evolution on a heterogeneous immune landscape
预测异质免疫环境中流感的演变
- 批准号:
10593425 - 财政年份:2022
- 资助金额:
$ 20.36万 - 项目类别:
Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigation
实时跟踪病毒进化,用于疫苗株选择和流行病学调查
- 批准号:
10687985 - 财政年份:2016
- 资助金额:
$ 20.36万 - 项目类别:
Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigation
实时跟踪病毒进化,用于疫苗株选择和流行病学调查
- 批准号:
10397121 - 财政年份:2016
- 资助金额:
$ 20.36万 - 项目类别:
Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigation
实时跟踪病毒进化,用于疫苗株选择和流行病学调查
- 批准号:
10616295 - 财政年份:2016
- 资助金额:
$ 20.36万 - 项目类别:
Understanding Transmission with Integrated Genetic and Epidemiologic Inference
通过综合遗传和流行病学推断了解传播
- 批准号:
9307943 - 财政年份:
- 资助金额:
$ 20.36万 - 项目类别:
Understanding Transmission with Integrated Genetic and Epidemiologic Inference
通过综合遗传和流行病学推断了解传播
- 批准号:
8796474 - 财政年份:
- 资助金额:
$ 20.36万 - 项目类别:
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