Modeling COVID-19 care capacity in a major health system

Hospital resources, especially critical care beds and ventilators, have been strained by additional demand throughout the COVID-19 pandemic. Rationing of scarce critical care resources may occur when available resource limits are exceeded. However, the dynamic nature of the COVID-19 pandemic and variability in projections of the future burden of COVID-19 infection pose challenges for optimizing resource allocation to critical care units in hospitals. Connecticut experienced a spike in the number of COVID-19 cases between March and June 2020. Uncertainty about future incidence made it difficult to predict the magnitude and duration of the increased COVID-19 burden on the healthcare system. In this paper, we describe a model of COVID-19 hospital capacity and occupancy that generates estimates of the resources necessary to accommodate COVID-19 patients under infection scenarios of varying severity. We present the model structure and dynamics, procedure for parameter estimation, and publicly available web application where we implemented the tool. We then describe calibration using data from over 3,000 COVID-19 patients seen at the Yale-New Haven Health System between March and July 2020. We conclude with recommendations for modeling tools to inform decision-making using incomplete information during future crises.