RAPID: Visual Analytics Approach to Real-Time Tracking of COVID-19
- 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)
$187,477Funder
National Science Foundation (NSF)Principal Investigator
Raju GottumukkalaResearch Location
United States of AmericaLead Research Institution
University of Louisiana at LafayetteResearch Priority Alignment
N/A
Research Category
Policies for public health, disease control & community resilience
Research Subcategory
Communication
Special Interest Tags
Data Management and Data Sharing
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
COVID-19 data, related to infection rates, at-risk populations, mobility, and commute dynamics are rapidly becoming available from several sources. However, there is a lack of interactive visual decision-making environments integrated with data-driven tools to help public health and community leaders understand how various factors such as physical distancing and other mitigation strategies, impact the spread of disease, help flatten the curve, enabling economic recovery while minimizing public health risk due to reopening. This project will develop visual analytic tools for tracking COVID-19 and propose balanced intervention strategies for effective containment of the outbreak.
The proposed visual analytics system integrates heterogeneous datasets and enables the application of relevant analytical models and data-engineering for decision support in a complex and evolving crisis. The objectives include the development of (1) forecasting models for recovery based on incidence, population vulnerabilities, mobility patterns, and mitigation activities, (2) social-media tools to understand public sentiment and risk perceptions, (3) visual interface for model-refinement & diagnosis through data engineering and visual analytics principles. The decision-making framework will offer new insights, close the gap between data and decisions, and is driven-by inputs from extensive partnerships & collaborations to improve reliability and usability.
The data-driven tools will help improve decision makersí understanding of disease dynamics from multiple variables. Epidemiologists could potentially leverage these insights to create higher-fidelity models based on interventional factors and their effect on population behaviors. Local authorities could also utilize the models to make life-saving decisions while minimizing impact to the economy. The project will enable new public and private partnerships including the City of New Orleans, and Industry Advisory Board of NSF Center for Visual and Decision Informatics. The project will benefit graduate and undergraduate students through hands-on research experience with the development of analytical products. The project outcomes will include analytics dashboards, source code, models, and data collected from multiple sources. The dashboards, project descriptions, and a list of data sources along with their metadata will be made publicly available on www.vastream.net for a period of two years. The public facing portion of the portal for COVID-19 component will be moved to Amazon cloud in event of disruptions from outages, for the duration of the project. A new public repository will be created on GitHub, and the source code and publicly available datasets will be made available on this project repository.
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 proposed visual analytics system integrates heterogeneous datasets and enables the application of relevant analytical models and data-engineering for decision support in a complex and evolving crisis. The objectives include the development of (1) forecasting models for recovery based on incidence, population vulnerabilities, mobility patterns, and mitigation activities, (2) social-media tools to understand public sentiment and risk perceptions, (3) visual interface for model-refinement & diagnosis through data engineering and visual analytics principles. The decision-making framework will offer new insights, close the gap between data and decisions, and is driven-by inputs from extensive partnerships & collaborations to improve reliability and usability.
The data-driven tools will help improve decision makersí understanding of disease dynamics from multiple variables. Epidemiologists could potentially leverage these insights to create higher-fidelity models based on interventional factors and their effect on population behaviors. Local authorities could also utilize the models to make life-saving decisions while minimizing impact to the economy. The project will enable new public and private partnerships including the City of New Orleans, and Industry Advisory Board of NSF Center for Visual and Decision Informatics. The project will benefit graduate and undergraduate students through hands-on research experience with the development of analytical products. The project outcomes will include analytics dashboards, source code, models, and data collected from multiple sources. The dashboards, project descriptions, and a list of data sources along with their metadata will be made publicly available on www.vastream.net for a period of two years. The public facing portion of the portal for COVID-19 component will be moved to Amazon cloud in event of disruptions from outages, for the duration of the project. A new public repository will be created on GitHub, and the source code and publicly available datasets will be made available on this project repository.
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.