RAPID: Collaborative Research: Using Phylodynamics and Line Lists for Adaptive COVID-19 Monitoring
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$50,000Funder
National Science Foundation (NSF)Principal Investigator
Aditya B PrakashResearch Location
United States of AmericaLead Research Institution
University of Virginia Main CampusResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Disease transmission dynamics
Special Interest Tags
N/A
Study Type
Not applicable
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
It has been difficult to track and control the COVID-19 pandemic due to various factors such as asymptomatic transmission, high incubation period, human mobility, weather patterns and limited number of tests available. Especially as the number of cases rise, it will become hard to monitor, and request quarantine appropriately, as experience in other countries shows. Hence, this project aims to improve COVID-19 monitoring by designing more targeted and adaptive testing and intervention in a data-driven fashion. With both monitoring and intervention applications, this project directly attacks the problem through development of processes and actions to address this pandemic and also model and understand its spread. Apart from the immediate applications to the COVID-19 pandemic, the tools developed should be more broadly useful for other infectious disease settings (e.g. influenza).
The team of Data Science, Network Science, Public Health and Phylogenetic analysis experts main approach for this question is to integrate several novel datasets via inference algorithms. The project focuses on two tasks: Task 1: Aligning phylodynamics data (PD) with line lists; and Task 2: Inferring transmission chains to new infections using aligned data. The teams prior works on interventions and monitoring have been highly successful in this regard. These inferred transmission chains naturally give guidance on whom to adaptively monitor and quarantine among the new infections. The project will release its methods as research code, which should be usable by both practitioners and modelers for faster monitoring under resource constraints.
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 team of Data Science, Network Science, Public Health and Phylogenetic analysis experts main approach for this question is to integrate several novel datasets via inference algorithms. The project focuses on two tasks: Task 1: Aligning phylodynamics data (PD) with line lists; and Task 2: Inferring transmission chains to new infections using aligned data. The teams prior works on interventions and monitoring have been highly successful in this regard. These inferred transmission chains naturally give guidance on whom to adaptively monitor and quarantine among the new infections. The project will release its methods as research code, which should be usable by both practitioners and modelers for faster monitoring under resource constraints.
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.