Teng Zhang, Kelly McFarlane, Jacqueline Vallon, Linying Yang, Jin Xie, Jose Blanchet, Peter Glynn, Kristan Staudenmayer, Kevin Schulman, David Scheinker. A model to estimate bed demand for COVID-19 related hospitalization. medRxiv 2020.03.24.20042762; doi: https://doi.org/10.1101/2020.03.24.20042762
Abstract
As of March 23, 2020 there have been over 354,000 confirmed cases of coronavirus disease 2019 (COVID-19) in over 180 countries, the World Health Organization characterized COVID-19 as a pandemic, and the United States (US) announced a national state of emergency.1, 2, 3 In parts of China and Italy the demand for intensive care (IC) beds was higher than the number of available beds.4, 5 We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is COVID-19+ and the rate at which COVID-19 is spreading in the population. Our approach was to design a tool with parameters that hospital leaders could adjust to reflect their local data and easily modify to conduct sensitivity analyses.
We developed a model to facilitate hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool. The model is implemented in R 3.5, RStudio, RShiny 1.4.0 and Python 3.7. The parameters used may be modified as data become available, for use at other institutions, and to generate sensitivity analyses.
We illustrate the use of the model by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an …
Authors
Teng Zhang, Kelly McFarlane, Jacqueline Vallon, Linying Yang, Jin Xie, Jose Blanchet, Peter Glynn, Kristan Staudenmayer, Kevin Schulman, David Scheinker
Publication date
2020/3/26
Journal
medRxiv
Pages
2020.03. 24.20042762
Publisher
Cold Spring Harbor Laboratory Press
