On Apr. 21, 2020, to fight the novel coronavirus pandemic, Cleveland Clinic and SAS announced they had created innovative models that help hospitals forecast patient volume, bed capacity, ventilator availability and more. The models, which are freely available via GitHub, provide timely, reliable information for hospitals and health departments to optimize health care delivery for COVID-19 and other patients and to predict impacts on supply chain, finance and other critical areas.

Unlike some forecasts that focus on a projection based on a single set of assumptions, these analytic models were used to create worst-case, best-case and most-likely scenarios, and can adjust in real time as the situation and data change. For example, the models can factor in social distancing’s dampening effect on disease spread.

Cleveland Clinic is using the models to support its decision making. With this information, Cleveland Clinic can predict and plan for future demands on the health system, such as ICU beds, personal protective equipment and ventilators. After reviewing possible COVID-19 surge scenarios generated by the models, Cleveland Clinic elected to activate a plan that prepared it for the worst-case scenario and has built a 1,000-bed surge hospital on its education campus for COVID-19 patients who don’t need ICU care. The hospital system also used the models to inform decisions about organizing and activating new labor pools.