  Predicting patient volumes in a hospital setting is a well-studied
application of time series forecasting. Existing tools usually make forecasts
at the daily or weekly level to assist in planning for staffing requirements.
Prompted by new COVID-related capacity constraints placed on our pediatric
hospital's emergency department, we developed an hourly forecasting tool to
make predictions over a 24 hour window. These forecasts would give our hospital
sufficient time to be able to martial resources towards expanding capacity and
augmenting staff (e.g. transforming wards or bringing in physicians on call).
Using Gaussian Process Regressions (GPRs), we obtain strong performance for
both point predictions (average R-squared: 82%) as well as classification
accuracy when predicting the ordinal tiers of our hospital's capacity (average
precision/recall: 82%/74%). Compared to traditional regression approaches, GPRs
not only obtain consistently higher performance, but are also robust to the
dataset shifts that have occurred throughout 2020. Hospital stakeholders are
encouraged by the strength of our results, and we are currently working on
moving our tool to a real-time setting with the goal of augmenting the
capabilities of our healthcare workers.
