Award Abstract #2027521

Collaborative Research: RAPID: Building a Spatiotemporal Platform for Rapid Response to COVID-19

NSF Directorate:
CSE - Directorate for Computer and Information Science and Engineering
NSF Division:

Division of Computer and Network Systems

Initial Amendment Date:

Latest Amendment Date:

Award Number:

2027521

Award Instrument:

Grant

Program Manager:

Behrooz Shirazi

Start Date:

End Date:

Awarded Amount to Date:

$99,956.00

Investigator(s):

Chaowei Yang [email protected] (Principal Investigator)

Sponsor:

George Mason University
4400 UNIVERSITY DR
FAIRFAX VA 220304422

NSF Program:
IUCRC-Indust-Univ Coop Res Ctr
Program Reference Code(s):
096Z
7914
Program Element Code(s):
158Y
Abstract:

The Spatiotemporal Innovation IUCRC develops novel spatiotemporal analytical tools to enable applications of national and global significance. In response to the COVID-19 crisis, Harvard University and George Mason University, university sites within this IUCRC, propose this collaborative project to collect and share COVID related data in near real time, conduct spatiotemporal analytics, and mine socioeconomic and environmental knowledge to facilitate decision support systems in response to the pandemic.

This project will build a unique cloud-based platform composed of a data collection subsystem for collecting global, high quality COVID-19-related data; spatiotemporal analytics tools for analyzing the disease evolution and socioeconomic patterns; and, modeling tools for assessing medical supplies and logistics. Through web access services, the platform will provide capabilities for easy access to the data collected as well as access to the developed spatiotemporal analytical and modeling tools. Such capabilities will facilitate quick production of data-driven decision support systems for community preparedness. This project has secured participation of 50+ international researchers in developing the proposed platform. These researchers will help collect and validate data, analyze how policies influence the outbreaks, how the Earth environment is impacted, and how to balance reopening of the economy and controlling the spreading of the disease in the U.S. based on experiences from Asia and Europe. Over 200 undergraduate volunteers, including many from underrepresented groups, are already involved in this project through Harvard’s Coronavirus Visualization Team efforts.

Data, information, and knowledge accumulated in this project have been, and will continue to be, archived long term in a comprehensive gateway (covid-19.stcenter.net). Such data include spatiotemporal distribution of confirmed cases, relevant social, economic and natural information from different resources, such as authoritative reports, news releases, Earth observation, and social media. Software and tools developed are posted on GitHub for open access. Sustained online collaboration is being conducted to produce replicable research using spatiotemporal analyses to mine patterns and relations between COVID-19 and social and natural factors for community response and preparedness.

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.