Prediction of Personal Protective Equipment Use in Hospitals During COVID-19
Eugene Furman, Alex Cressman, Saeha Shin, Alexey Kuznetsov, Fahad, Razak, Amol Verma, Adam Diamant

TL;DR
This paper presents a predictive model for PPE demand in hospitals during COVID-19 by analyzing patient admission data and treatment plans, aiding supply management during the pandemic.
Contribution
It introduces a queue-based modeling approach to estimate PPE needs based on patient workload and treatment protocols, specifically tailored for COVID-19 hospital settings.
Findings
Gloves and masks constitute about 90% of PPE demand.
Demand for gloves is driven by patient-practitioner interactions.
86% of mask demand is due to practitioners wearing masks when not with patients.
Abstract
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…
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