Navigating hospitals safely through the COVID-19 epidemic tide: predicting case load for adjusting bed capacity.

BACKGROUND: The pressures exerted by the pandemic of COVID-19 pose an unprecedented demand on health care services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. We here describe methods used by a university hospital to forecast caseloads and time to peak incidence. METHODS: We developed a set of models to forecast incidence among the hospital catchment population and describe the COVID-19 patient hospital care-path. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care path model according to expert opinion (static model). Once sufficient local data were available, trends for the time dependent effective reproduction number were fitted and the care-path was re-parameterized using hazards for real patient admission, referrals, and discharge (dynamic model). RESULTS: The static model, deployed before the epidemic, exaggerated the bed occupancy (general wards 116 forecasted vs 66 observed, ICU 47 forecasted vs 34 observed) and predicted the peak too late (general ward forecast April 9, observed April 8, ICU forecast April 19, observed April 8). After April 5, the dynamic model could be run daily and precision improved with increasing availability of empirical local data. CONCLUSIONS: The models provided data-based guidance in the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.