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RAPID:Genomic Variation Analysis of Coronavirus to Better Understand the Spread of COVID-19

RAPID:Genomic Variation Analysis of Coronavirus to Better Understand the Spread of COVID-19
RAPID:冠状病毒的基因组变异分析,以更好地了解 COVID-19 的传播
批准号:
2027667
负责人:
Jing Li
金额:
$12.44万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-05-01 至 2024-10-31

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中文摘要
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英文摘要
Currently, there is a world-wide pandemic of the Coronavirus Disease 2019 (COVID-19) that is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of April 2, 2020, there have been more than one million confirmed cases of COVID-19 and more than 55,000 deaths. These numbers are increasing rapidly. Yet, the short time since the beginning of this outbreak limits our understanding of how SARS-CoV-2 spreads. The objective of this project is to address this gap. In response to evidence that the rate of infections among healthcare providers is alarmingly high, this project will expose transmission patterns in the hospital setting. In collaboration with the Cleveland Clinic, the investigators will have access to genomic, epidemiological, and clinical patient data. Joint computational analysis of this data will result in a computational model that will provide insights into transmission patterns in hospitals, such as from patients to doctors and health workers, or from doctors to doctors. The activities in this project will be organized along three Aims. Aim 1: Perform whole-genome sequencing of SARS-CoV-2 samples collected at the Cleveland Clinic. The data will be augmented by existing SARS-CoV-2 sequence data from multiple online databases, as well as existing sequence data from other species. Aim 2: Build an analysis pipeline and perform evolutionary analysis of genomic data obtained in Aim 1. Aim 3: Perform joint analysis of genomic, epidemiological, and clinical data to infer transmission patterns. One of the unique advantages of the project is the ability to directly link identified genomic strains to clinical data. The activities have direct clinical implications for better protecting healthcare workers and minimizing the overall rate of infections in the hospital setting. Findings from this project will be shared with the research community at large to aid further analysis in other hospitals as more data becomes available during this pandemic.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(3)
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科研奖励(0)
会议论文
DOI: 10.1007/s11262-023-02011-0
发表时间: 2023-06
期刊: Virus Genes
影响因子: 1.6
作者: [Kim El-Haddad;T. M. Adhikari;Zheng Jin Tu;Yu-Wei Cheng;Xiaoyi Leng;Xiangyi Zhang;D. Rhoads;J. Ko;S. Worley;Jing Li;B. Rubin;Frank P Esper]
通讯作者: Kim El-Haddad;T. M. Adhikari;Zheng Jin Tu;Yu-Wei Cheng;Xiaoyi Leng;Xiangyi Zhang;D. Rhoads;J. Ko;S. Worley;Jing Li;B. Rubin;Frank P Esper
CAREER: Towards Safety-Critical Real-Time Systems with Learning Components
  • 批准号:
    2340171
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $53.27万
  • 财政年份:
    2024
  • 负责人:
    Jing Li
  • 依托单位:
Collaborative Research: RUI: Structured Population Dynamics Subject to Stoichiometric Constraints
PIPP Phase I: Comprehensive, Integrated, Intelligent System for Early and Accurate Pandemic Prediction, Prevention, and Preparation at Personal and Population Levels
  • 批准号:
    2200255
  • 项目类别:
    Standard Grant
  • 资助金额:
    $100.0万
  • 财政年份:
    2022
  • 负责人:
    Jing Li
  • 依托单位:
NSF-BSF: Collaborative Research: Market Conduct in Technology Adoption in the Automobile Industry
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