Forecasting Patient Flows with Pandemic Induced Concept Drift using Explainable Machine Learning. (arXiv:2211.00739v1 [cs.LG])

Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and
Emergency Departments (EDs) is important for effective resourcing and patient
care. However, correctly estimating patient flows is not straightforward since
it depends on many drivers. The predictability of patient arrivals has recently
been further complicated by the COVID-19 pandemic conditions and the resulting
lockdowns. This study investigates how a suite of novel quasi-real-time
variables like Google search terms, pedestrian traffic, the prevailing
incidence levels of influenza, as well as the …

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