EAGER: Data-Driven Susceptible-Exposed-Infected-Recovered-Infected (SEIRI) Modeling and Hospital Planning and Operations for COVID-19 Pandemic
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2027677
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Key facts
Disease
COVID-19Start & end year
20202022Known Financial Commitments (USD)
$299,999Funder
National Science Foundation (NSF)Principal Investigator
Yongpei GuanResearch Location
United States of AmericaLead Research Institution
University of FloridaResearch Priority Alignment
N/A
Research Category
Policies for public health, disease control & community resilience
Research Subcategory
Policy research and interventions
Special Interest Tags
Innovation
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
Engineering - This EArly-concept Grant for Exploratory Research (EAGER) project will provide new methods for decision-support of healthcare operations in response to the COVID-19 pandemic. Because the coronavirus is highly contagious and results in severe disease in a relatively high percentage of those infected, healthcare resources, particularly hospital beds and essential personnel, are expected to be at or beyond capacity at the height of the epidemic. This project develops quantitative methods to estimate how interventions such as screening and surveillance via telemedicine and offsite testing and diagnosis can reduce the load on hospital personnel. The models developed are expected to improve the ability of hospital decision makers to predict equipment needs to ensure critical hospital personnel are protected and able to provide effective in-patient treatment.
This EAGER award supports fundamental research in methods to integrate a susceptible-exposed-infected-recovered-infected (SEIRI) virus transmission model with a risk-averse sequential operational planning model for patient beds, staffing and personal protective equipment (PPE) in hospitals. The operational planning model comprises a stochastic optimization model that includes deployment of telemedicine for initial screening and surveillance, drive-through testing for diagnosis, and in-patient hospital care for those with severe disease. The project represents a tight collaboration between the PIs and Mayo Clinic Jacksonville (MCJ). The model parameters will be updated dynamically using MCJ data as part of a pilot project, and once tested, the decision-support system will be made available online for other hospitals in the nation and the world to use.
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 EAGER award supports fundamental research in methods to integrate a susceptible-exposed-infected-recovered-infected (SEIRI) virus transmission model with a risk-averse sequential operational planning model for patient beds, staffing and personal protective equipment (PPE) in hospitals. The operational planning model comprises a stochastic optimization model that includes deployment of telemedicine for initial screening and surveillance, drive-through testing for diagnosis, and in-patient hospital care for those with severe disease. The project represents a tight collaboration between the PIs and Mayo Clinic Jacksonville (MCJ). The model parameters will be updated dynamically using MCJ data as part of a pilot project, and once tested, the decision-support system will be made available online for other hospitals in the nation and the world to use.
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.