  For hospitals, realistic forecasting of bed demand during impending epidemics
of infectious diseases is essential to avoid being overwhelmed by a potential
sudden increase in the number of admitted patients. Short-term forecasting can
aid hospitals in adjusting their planning and freeing up beds in time. We
created an easy-to-use online on-request tool based on local data to forecast
COVID-19 bed demand for individual hospitals. The tool is flexible and
adaptable to different settings. It is based on a stochastic compartmental
model for estimating the epidemic dynamics and coupled with an exponential
smoothing model for forecasting. The models are written in R and Julia and
implemented as an R-shiny dashboard. The model is parameterized using COVID-19
incidence, vaccination, and bed occupancy data at customizable geographical
resolutions, loaded from official online sources or uploaded manually. Users
can select their hospital's catchment area and adjust the number of COVID-19
occupied beds at the start of the simulation. The tool provides short-term
forecasts of disease incidence and past and forecasted estimation of the
epidemic reproductive number at the chosen geographical level. These quantities
are then used to estimate the bed occupancy in both general wards and intensive
care unit beds. The platform has proven efficient, providing results within
seconds while coping with many concurrent users. By providing ad-hoc, local
data informed forecasts, this platform allows decision-makers to evaluate
realistic scenarios for allocating scarce resources, such as ICU beds, at
various geographic levels.
