Award Abstract #2027678
RAPID: Combining Big Data in Transportation with Hospital Health Data to Build Realistic "Flattening the Curves" Models during the COVID-19 Outbreak

NSF Org:	CMMI Div Of Civil, Mechanical, & Manufact Inn
Initial Amendment Date:	April 20, 2020
Latest Amendment Date:	April 20, 2020
Award Number:	2027678
Award Instrument:	Standard Grant
Program Manager:	Walter Peacock CMMI Div Of Civil, Mechanical, & Manufact Inn ENG Directorate For Engineering
Start Date:	May 1, 2020
End Date:	April 30, 2021 (Estimated)
Awarded Amount to Date:	$89,240.00
Investigator(s):	Debbie Niemeier niemeier@umd.edu (Principal Investigator) Kartik Kaushik (Co-Principal Investigator)
Sponsor:	University of Maryland, College Park 3112 LEE BLDG 7809 Regents Drive College Park, MD 20742-5141 (301)405-6269
NSF Program(s):	COVID-19 Research
Program Reference Code(s):	036E, 041E, 042E, 096Z, 1576, 7914, 9102
Program Element Code(s):	158Y

ABSTRACT

The outbreak of COVID-19 in the U.S. provides an important opportunity for researchers to improve flattening curve models which can be used to assess and even spatially optimize health care during a rapidly expanding pandemic. This Rapid Response Research (RAPID) project will take advantage of the large-scale availability of location-sensing devices and apps that produce big data on mobility patterns that can be used to better optimize the use of healthcare facilities. This research brings together rapidly unfolding health data with real-time data on mobility. We will examine how these two critical data resources can be linked to better inform policy, identify emerging hotspots, and target critical actions during a pandemic. This research will help public officials to better understand and adapt to changing conditions as a health emergency arises and expands.

The spread of the ?flattening curves? graphic was significant in promoting public understanding of the criticality of social distancing. These curves, however, were based on simulated data. This research will collect and examine mobility data and public health data to model flattening curves using real data. We combine big data from location-based apps and cellphones with Electronic Medical records from UMMS hospitals, including data on COVID-19 tests, and patient demographics and prognostics. New modeling approaches that quantitatively measure change in collective movement behaviors in response to the fast-evolving COVID-19 outbreak will be linked to hospital usage and capacity. The methods of this research will extend our knowledge of highly integrated systems, like transportation and health, and better prepare the public for future disasters.

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.

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