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Volume 10
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10.3390/healthcare10061120
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Article

A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic

by
Egbe-Etu Etu
1,*,
Leslie Monplaisir
2,
Sara Masoud
2,
Suzan Arslanturk
3,
Joshua Emakhu
2,
Imokhai Tenebe
4,
Joseph B. Miller
5,
Tom Hagerman
5,
Daniel Jourdan
5 and
Seth Krupp
5
1
Department of Marketing & Business Analytics, San Jose State University, One Washington Square, San Jose, CA 95192, USA
2
Department of Industrial & Systems Engineering, Wayne State University, 4815 4th Street, Detroit, MI 48202, USA
3
Department of Computer Science, Wayne State University, 5057 Woodward Ave., Detroit, MI 48202, USA
4
Texas Commission on Environmental Quality, Critical Infrastructure Division, 1200 Park 35 Circle, Austin, TX 78711, USA
5
Departments of Emergency Medicine and Internal Medicine, Henry Ford Hospital, 2799 W Grand Blvd, Detroit, MI 48202, USA
*
Author to whom correspondence should be addressed.
Healthcare 2022, 10(6), 1120; https://doi.org/10.3390/healthcare10061120
Submission received: 12 May 2022 / Revised: 3 June 2022 / Accepted: 14 June 2022 / Published: 16 June 2022
(This article belongs to the Section Health Informatics and Big Data)
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Abstract

The COVID-19 pandemic has heightened the existing concern about the uncertainty surrounding patient arrival and the overutilization of resources in emergency departments (EDs). The prediction of variations in patient arrivals is vital for managing limited healthcare resources and facilitating data-driven resource planning. The objective of this study was to forecast ED patient arrivals during a pandemic over different time horizons. A secondary objective was to compare the performance of different forecasting models in predicting ED patient arrivals. We included all ED patient encounters at an urban teaching hospital between January 2019 and December 2020. We divided the data into training and testing datasets and applied univariate and multivariable forecasting models to predict daily ED visits. The influence of COVID-19 lockdown and climatic factors were included in the multivariable models. The model evaluation consisted of the root mean square error (RMSE) and mean absolute error (MAE) over different forecasting horizons. Our exploratory analysis illustrated that monthly and weekly patterns impact daily demand for care. The Holt–Winters approach outperformed all other univariate and multivariable forecasting models for short-term predictions, while the Long Short-Term Memory approach performed best in extended predictions. The developed forecasting models are able to accurately predict ED patient arrivals and peaks during a surge when tested on two years of data from a high-volume urban ED. These short- and long-term prediction models can potentially enhance ED and hospital resource planning.
Keywords: COVID-19; emergency department; forecasting; deep learning; emerging infectious disease COVID-19; emergency department; forecasting; deep learning; emerging infectious disease

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MDPI and ACS Style

Etu, E.-E.; Monplaisir, L.; Masoud, S.; Arslanturk, S.; Emakhu, J.; Tenebe, I.; Miller, J.B.; Hagerman, T.; Jourdan, D.; Krupp, S. A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic. Healthcare 2022, 10, 1120. https://doi.org/10.3390/healthcare10061120

AMA Style

Etu E-E, Monplaisir L, Masoud S, Arslanturk S, Emakhu J, Tenebe I, Miller JB, Hagerman T, Jourdan D, Krupp S. A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic. Healthcare. 2022; 10(6):1120. https://doi.org/10.3390/healthcare10061120

Chicago/Turabian Style

Etu, Egbe-Etu, Leslie Monplaisir, Sara Masoud, Suzan Arslanturk, Joshua Emakhu, Imokhai Tenebe, Joseph B. Miller, Tom Hagerman, Daniel Jourdan, and Seth Krupp. 2022. "A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic" Healthcare 10, no. 6: 1120. https://doi.org/10.3390/healthcare10061120

APA Style

Etu, E.-E., Monplaisir, L., Masoud, S., Arslanturk, S., Emakhu, J., Tenebe, I., Miller, J. B., Hagerman, T., Jourdan, D., & Krupp, S. (2022). A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic. Healthcare, 10(6), 1120. https://doi.org/10.3390/healthcare10061120

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