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

导致新冠肺炎的冠状病毒于2019年11月首次在人类身上检测到，并迅速发展成为一场大流行。迫切需要提高准确跟踪和预测疾病传播的能力。然而，对检测时间和暴露时间等经典流行病学数据的分析提供的洞察力有限。这一快速反应研究(RAPID)项目旨在基于基因组数据的分析发现疾病传播模式，提供准确的传播集群识别，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洞察。该项目的具体目标是：(1)开发基于感染宿主的病毒病原体的基因组信息来推断宿主网络的方法。特别是，本研究的重点是重建一个加权有向图，该图的节点代表宿主，边权重反映相应病原体之间的进化距离。(2)开发发现传输簇和识别主机网络中关键节点的方法。本文的研究重点是深入学习算法，用于识别传播簇，发现在疾病暴发中起关键作用的宿主网络节点。(3)依托已开发的方法，对已公开发布的新冠肺炎数据集进行分析。概述的工作结果预计将对了解冠状病毒的传播和传播产生立竿见影的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.