  Demand for Personal Protective Equipment (PPE) such as surgical masks,
gloves, and gowns has increased significantly since the onset of the COVID-19
pandemic. In hospital settings, both medical staff and patients are required to
wear PPE. As these facilities resume regular operations, staff will be required
to wear PPE at all times while additional PPE will be mandated during medical
procedures. This will put increased pressure on hospitals which have had
problems predicting PPE usage and sourcing its supply. To meet this challenge,
we propose an approach to predict demand for PPE. Specifically, we model the
admission of patients to a medical department using multiple independent
queues. Each queue represents a class of patients with similar treatment plans
and hospital length-of-stay. By estimating the total workload of each class, we
derive closed-form estimates for the expected amount of PPE required over a
specified time horizon using current PPE guidelines. We apply our approach to a
data set of 22,039 patients admitted to the general internal medicine
department at St. Michael's hospital in Toronto, Canada from April 2010 to
November 2019. We find that gloves and surgical masks represent approximately
90% of predicted PPE usage. We also find that while demand for gloves is driven
entirely by patient-practitioner interactions, 86% of the predicted demand for
surgical masks can be attributed to the requirement that medical practitioners
will need to wear them when not interacting with patients.
