RAPID: Early Detection of Disease Outbreaks using Self-Organizing Patterns - COVID-19
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2028051
Grant search
Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$200,000Funder
National Science Foundation (NSF)Principal Investigator
Sylvia ThomasResearch Location
United States of AmericaLead Research Institution
University of South FloridaResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Impact/ effectiveness of control measures
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 - This project will advance national health through the development of a robust, predictive model to graphically represent the spread of COVID-19 through an innovative integration of public health information and social media data. This project develops a predictive modeling tool to visually represent the spread of COVID-19 or other potential pandemics utilizing artificial intelligence techniques to support data gathering, analysis and representation of the outcomes. The societal benefit is significant if the researchers are successful in developing a model that utilizes traditional public health data integrated with social media data to expeditiously create a reliable prediction of disease spread. This project will contribute towards building an open source database that can be accessed anywhere across the globe while providing early warning detection and signals to government agencies.
This RAPID project develops a large-scale pandemic model to enable data sharing, using AI-based approaches, and predictive modeling. Specifically, the project proposes a model for rapid and early disease detection, by combining three novel intellectual approaches to outbreak detection. These three approaches include 1) Self-organizing systems theory to detect nascent pattern formation; 2) Leveraging topical proximity in research communications over geospatial proximity of infected individuals; and 3) Loss of complexity in topical networks as an indicator of an ?unhealthy? system with an impending outbreak. This integrated approach has not been previously attempted in efforts to track disease outbreak. The proposed methodology is expected to produce a working model within the 2nd quarter of the project.
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.
This RAPID project develops a large-scale pandemic model to enable data sharing, using AI-based approaches, and predictive modeling. Specifically, the project proposes a model for rapid and early disease detection, by combining three novel intellectual approaches to outbreak detection. These three approaches include 1) Self-organizing systems theory to detect nascent pattern formation; 2) Leveraging topical proximity in research communications over geospatial proximity of infected individuals; and 3) Loss of complexity in topical networks as an indicator of an ?unhealthy? system with an impending outbreak. This integrated approach has not been previously attempted in efforts to track disease outbreak. The proposed methodology is expected to produce a working model within the 2nd quarter of the project.
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.