Industrial & Systems Engineering Professor Yongpei Guan, Ph.D. and Assistant Professor Xiang Zhong, Ph.D., have received an Early Concept Grant for Exploratory Research from the National Science Foundation in support of their study on data-driven Susceptible-Exposed-Infected-Recovered-Infected (SEIRI) modeling and risk averse sequential planning for patient beds, staffing and personal protective equipment (PPE) in hospitals to combat the Coronavirus Disease 2019 (COVID-19).
COVID-19 has rapidly spread beyond borders and was classified as a pandemic in early March. As a result, the nation’s healthcare system has been on the frontlines helping to fight the virus. Due to the expansive nature of the disease, the influx of hospitalized patients has created a significant challenge for hospital planning and operations. This challenge imposes a tremendous threat to human health and life.
In order to successfully care for each patient, hospitals are facing questions such as how many general wards and intensive care units are needed, how much PPE needs to be purchased and how to optimally allocate these resources to ultimately reduce fatalities and help as many people recover from the virus as possible.
Dr. Guan and Dr. Zhong are working with Mayo Clinic Jacksonville on a pilot project that will provide a toolset to help hospitals around the world fight COVID-19 and create a framework for preparations for future outbreaks of other fast-spreading diseases.
“This integrated study will benefit COVID-19 and other similar highly contagious disease patients,” Dr. Guan said. “By conducting this study, the team’s effort helps several disciplines, including, but not limited to, public health, government policy, and hospital operations.”
The proposed data-driven decision-support system will allow healthcare workers to determine how to optimally use limited resources to mitigate risk and minimize adverse outcomes throughout the entirety of the disease progression, from early screening to diagnosis and treatment. The model will provide an advanced quantitative analysis of the SEIRI model, which is adaptive to control measures and can be utilized for regional forecast targeting a hospitals catchment area. This systematic approach will better assist hospitals with planning and operations such as ensuring reliable PPE and staff planning, while remaining cost-effective.
The overarching goal of this project is to improve patient outcomes, and enhance scalability and capacity within a rapid response time. By allowing hospitals to better prepare and allocate the proper amount of resources needed based on confirmed admission numbers, medical care workers are then able to focus on the quality and quantity of patient care and ultimately, enhance total recovery.