RAPID: Early Detection of Disease Outbreaks using Self-Organizing Patterns – COVID-19

Award ID: 2028051

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.

NSF Program Director: Rebecca Shearman