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
• 负责人: Trevor BC Bedford
• 金额: $ 41.81万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-23 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10397121
• 关键词: 2019-nCoV; African; American; COVID-19 pandemic; Communicable Diseases; Contact Tracing; Data; Data Set; Dengue Virus; Disease Outbreaks; Early Diagnosis; Ebola; Ebola virus; Epidemic; Epidemiologist; Epidemiology; Evolution; Future; Genes; Genetic; Genomics; Geography; Goals; Human; Immunity; Influenza; Methodology; Methods; Modeling; Monitor; Mumps virus; Pattern; Population; Process; Public Health; Time; Tuberculosis; United States; Update; Vaccines; Variant; Viral; Viral Genome; Viral Load result; Virus; Work; ZIKA; Zika Virus; bioinformatics pipeline; epidemiology study; genomic data; genomic epidemiology; improved; influenza virus vaccine; influenzavirus; innovation; novel; pandemic disease; pathogen; pathogenic virus; public health intervention; response; seasonal influenza; tool; transmission process; vaccine efficacy; viral genomics; web site

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.

项目概要 病毒病原体对全球公共卫生构成持久威胁。该项目旨在利用病毒基因组数据 提高对正在进行的病毒进化的了解，并做出可操作的推论，以减少全球范围内的病毒传播 病毒传染病的负担。为了与公共卫生干预措施相关，对病毒进行分析 序列数据在计算和传播方面都需要非常快。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 从流感基因序列数据中分析并从爆发序列数据中分析传播模式。 在当前的项目期间（2016-2021），我们开发了一个名为 Nextstrain 的实时分析平台， 提供多种病原体的最新分析，包括流感病毒、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。生物信息管道开发通过 Nextstrain 可由学术团体和公共卫生实验室重复使用，并且结果分析可通过 网站 nextstrain.org。 在即将到来的项目期间（2021-2026），我们将完善预测流感菌株动态的方法 病毒。监测和预测病毒株的进化至关重要。新的抗原变体 部分逃脱人类先前免疫力的流感出现并迅速席卷病毒 人口。这些菌株对疫苗衍生的免疫不太敏感，因此抗原进化导致 需要经常更新季节性流感疫苗。该项目旨在完善预测方法 菌株动态并预测未来流感人群的构成。这个预测特别 relevant to influenza vaccine strain selection, as a vaccine strain is chosen for the Northern Hemisphere in 二月部署于次年冬季。准确的预测将有助于季节性疫苗匹配 流感病毒，从而提高疫苗功效。技术创新侧重于将模型扩展到 跨越不同的病毒、不同的基因片段并整合空间动力学。 在西非埃博拉疫情、美国寨卡疫情或非典等疫情爆发的情况下—— CoV-2大流行，公共卫生干预的重点集中在早期诊断、接触者追踪、隔离和 治疗。对传播动力学的流行病学了解对于疫情爆发至关重要 回复。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。地理分布特别适合基因组推论。该项目将开发工具 从爆发序列数据中做出流行病学推断。这些方法将继续被部署 通过 Nextstrain 平台，世界各地的流行病学家可以分析他们自己的数据集。 基因组流行病学有潜力真正为疫情应对提供信息。 Nextstrain 发挥了重要作用 美国和世界范围内的 SARS-CoV-2 基因组流行病学。准确性和准确性的改进 该平台的功能将得到充分利用。