Overcoming a pandemic:

Abstract

COVID-19, a novel disease that spreads across the globe, has posed multiple challenges to the healthcare systems around the world. Due to the lack of understanding of the spread and management of this disease, one major challenge is for healthcare systems to anticipate the volumes and needs of patients infected with the disease. In order to provide insights into optimal allocation of resources from preparing ChristianaCare for the pandemic to the recovery of the healthcare system, industrial engineering and predictive modeling approaches are used. This paper discusses five interrelated studies that utilize various techniques to inform multiple aspects of the healthcare system in order to be better prepared for the pandemic.

Prediction Model for COVID-19 Patient Visits in the Ambulatory Setting

“…SIR models have also been applied to inpatient settings to predict hospital capacity regarding admissions, ICU beds, and ventilators [11,13,15,16]. In addition to SIR models, the current literature on predicting patient volume varies from descriptive statistics to Discrete-event Simulation (DES), Markov modeling, and advanced time series models, with most of the studies that have used time series forecasting models focusing on emergency department and hospital admissions [17]. Time series forecasting in ambulatory visits prior to the COVID19 pandemic have been described in a few reports but other types of modeling for both in-person and telehealth visits are lacking [18][19][20][21][22][23][24][25].…”

“…The most used method for time series forecasting is the Box-Jenkins method otherwise known as the AutoRegressive Integrated Moving Average (ARIMA) model [26]. The ARIMA model has been used for its simplicity and flexibility in capturing linear patterns in a time series [17,[19][20][21][22].…”