Abstract
Health care systems are at the frontline to fight the COVID-19 pandemic. An emergent question for each hospital is how many general ward and intensive care unit beds are needed and how much personal protective equipment to be purchased. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a computer simulation approach to evaluating bed utilizations and the corresponding supply needs based on the operational considerations and constraints in individual hospitals. We built a data-driven SEIR model which is adaptive to control policies and can be utilized for regional forecast targeting a specific hospital’s catchment area. The forecast model was integrated into a discrete-event simulation which modeled the patient flow and the interaction with hospital resources. We tested the simulation model outputs against patient census data from UF Health Jacksonville, Jacksonville, FL. Simulation results were consistent with the observation that the hospital has ample bed resources to accommodate the regional COVID patients. After validation, the model was used to predict future bed utilizations given a spectrum of possible scenarios to advise bed planning and stockpiling decisions. Lastly, how to optimally allocate hospital resources to achieve the goal of reducing the case fatality rate while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.
Area per Ligand as a Function of Nanoparticle Radius: A Theoretical and Computer Simulation Approach
