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.

项目总结