RAPID: Combining Big Data in Transportation with Hospital Health Data to Build Realistic "Flattening the Curves" Models during the COVID-19 Outbreak
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2027678
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$89,240Funder
National Science Foundation (NSF)Principal Investigator
Debbie NiemeierResearch Location
United States of AmericaLead Research Institution
University of Maryland College ParkResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Impact/ effectiveness of control measures
Special Interest Tags
Data Management and Data Sharing
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
Engineering - 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.
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.