RAPID: Methods for Reconstructing Disease Transmissions from Viral Genomic Data with Application to COVID-19
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2027773
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$100,000Funder
National Science Foundation (NSF)Principal Investigator
Haris VikaloResearch Location
United States of AmericaLead Research Institution
University of Texas at AustinResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Pathogen genomics, mutations and adaptations
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Computer and Information Science and Engineering - 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.
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.