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RAPID: Methods for Reconstructing Disease Transmissions from Viral Genomic Data with Application to COVID-19

RAPID：从病毒基因组数据重建疾病传播的方法并应用于 COVID-19

基本信息

批准号：
2027773
负责人：
Haris Vikalo
金额：
$ 10万
依托单位：
University of Texas at Austin
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
起止时间：
2020-05-15 至 2022-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027773&HistoricalAwards=false
关键词：
RAPID Methods Reconstructing Disease Transmissions

项目摘要

The coronavirus causing COVID-19 was first detected in humans in November 2019 and rapidly developed into a pandemic. There is an urgent need to enhance the ability to precisely track and predict spread of the disease. However, analysis of classical epidemiological data such as the time of testing and lengths of exposure provides limited insight. This Rapid Response Research (RAPID) project aims to enable discovery of disease transmission patterns based on analysis of genomic data, provide accurate identification of transmission clusters, and enable detection of critical nodes in a network of pathogen hosts while also providing insight into pathogen-mutation processes that occur during the spread of the disease.The specific aims of this project are to: (1) Develop methods for the inference of a network of hosts based on genomic information about viral pathogens infecting them. In particular, this research thrust is focused on the reconstruction of a weighted directed graph whose nodes represent hosts and edge weights reflect evolutionary distance between corresponding pathogens. (2) Develop methods for the discovery of transmission clusters and identification of critical nodes in the host network. The focus of this research thrust is on deep-learning algorithms for the identification of transmission clusters, and discovery of the host network nodes that played a pivotal role in the disease outbreak. (3) Relying on the developed methods, analyze publicly available COVID-19 datasets. The results of the outlined work are expected to have an immediate impact on the understanding of the coronavirus transmission and spread.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.

导致COVID-19的冠状病毒于2019年11月首次在人体中发现，并迅速发展为大流行。迫切需要提高精确跟踪和预测疾病传播的能力。然而，对经典流行病学数据（如检测时间和暴露时间）的分析提供的见解有限。这个快速反应研究（Rapid）项目旨在基于基因组数据分析发现疾病传播模式，提供传播集群的准确识别，并能够检测病原体宿主网络中的关键节点，同时还提供对疾病传播过程中发生的病原体突变过程的深入了解。该项目的具体目标是：(1)开发基于感染病毒病原体的基因组信息推断宿主网络的方法。特别是，本研究的重点是加权有向图的重建，其节点代表宿主，边权反映相应病原体之间的进化距离。(2)研究发现传输集群和识别主机网络中关键节点的方法。这项研究的重点是用于识别传播集群的深度学习算法，以及在疾病爆发中发挥关键作用的主机网络节点的发现。(3)根据开发的方法，分析公开可用的COVID-19数据集。概述工作的结果预计将对了解冠状病毒的传播和传播产生直接影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。