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.
