An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system

• Published in: PloS one Vol. 16; no. 10; p. e0258710
• Main Authors: Chertok, Daniel, Konchak, Chad, Shah, Nirav, Singh, Kamaljit, Au, Loretta, Hammernik, Jared, Murray, Brian, Solomonides, Anthony, Wang, Ernest, Halasyamani, Lakshmi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.10.2021; Public Library of Science (PLoS)
• Subjects: Accuracy; Biology and Life Sciences; Comprehensive Health Care; Coronaviruses; COVID-19; COVID-19 - embryology; COVID-19 vaccines; Disease transmission; Emergency management; Engineering and Technology; Epidemics; Epidemiology; Forecasting; Health care; Health planning; Health risks; Humans; Illinois; Immunization; Infection control; Infectious diseases; Medicine and Health Sciences; Models, Theoretical; Mortality; Pandemics; Physical Sciences; Planning; Population; Prediction models; Public health; Regional development; Research and Analysis Methods; SARS-CoV-2; Social aspects; Supply chains; Ventilators; Illinois; United States > US; Chicago Illinois
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0258710

An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.