Examining Surge Capacity in the COVID-19 Pandemic

As some healthcare systems approach collapse under the burden of the COVID-19 pandemic, a pressing need exists for tools modeling the capacity of acute and critical care systems during the COVID-19 pandemic. Researchers from Mount Sinai Hospital describe how they developed an online tool to estimate the maximum number of COVID-19 cases that could be managed per day within the catchment area served by a healthcare system given acute and critical care resource availability. Giannakeas, et al. (2020) sought to develop an online tool to estimate the maximum number of COVID-19 cases that could be managed per day within the catchment area served by a healthcare system, given acute- and critical-care resource availability.

The researchers modeled steady-state patient-flow dynamics constrained by the number of acute care beds, critical care beds, and mechanical ventilators available for COVID-19–infected patients seeking healthcare during the pandemic. Parameters for patient-flow dynamics were extracted from evolving data on COVID-19 and assumptions based on expert guidance.

The tool determines the maximum number of COVID-19 cases that could be managed per day within the catchment area served by a healthcare system, where the rate of patients with COVID-19 who are being admitted or transferred to acute-care or critical-care or requiring mechanical ventilation (“patients in”) equals the maximum daily turnover of each of those resources available for patients with COVID-19 (“patients out”). These estimates represent the maximum steady-state constraints imposed by these limited resources being managed by a health care system or hospital. Resources available for patients with COVID-19 should account for the proportion of existing staffed resources that could be made maximally available to support patients with COVID-19 plus any additional staffed surge capacity.

An accompanying editorial from Tufts Medical Center says that public health decision-making during the COVID-19 pandemic involves tradeoffs. Models have been developed to inform administrative policy decisions based on demand for hospital resources and other important considerations when peak demand occurs. The editorial discusses the fundamental differences among three models currently in use. As Wong (2020 observes, "With 2 million confirmed cases and more than 100,000 deaths worldwide, the severe acute respiratory syndrome coronavirus-2 (SARS–CoV-2) pandemic has generated fear, uncertainty, and doubt as the world has witnessed COVID-19–related deaths and overwhelmed hospitals in Wuhan, China; Italy; and New York City. With no preexisting herd immunity, vaccine, or proven antiviral treatment, public health decision making must rely on mitigation through social distancing. Such public health decisions cannot be answered by simple epidemiologic methods, because they involve 'tradeoffs, uncertainty, and values,' leading to the use of models to inform administrative and policy decision makers. At the behest of decision makers at the local, regional, hospital system, and national levels, all the models discussed herein have been designed to forecast demand for hospital resources—namely acute and critical care beds and mechanical ventilators—to understand hospital capacity constraints and to determine when peak demand will occur. Rapidly developed to be fit for purpose and user friendly, these models all inform local decision makers but have been used worldwide and continue to evolve. However, they differ fundamentally in their methodological approaches and the degree to which their projections can be customized to local context. They also are all exemplars of British statistician George E.P. Box's famous aphorism, 'All models are wrong, but some are useful.'"


Giannakeas V, et al. Estimating the maximum capacity of COVID-19 cases manageable per day given a healthcare system's constrained resources. Ann Intern Med. April 16, 2020.

Wong JB. Pandemic Surge Models in the Time of Severe Acute Respiratory Syndrome Coronavirus-2: Wrong or Useful? Ann Intern Med. April 16, 2020

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