Predicting Local Demand for COVID-19 Inpatient Care with a Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model

• Activity Number: 90 - Novel Statistical Methods for COVID Pandemic and Other Current Health Policy Issues
• Type: Contributed
• Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Health Policy Statistics Section
• Author(s): Stella Self* and Rongjie Huang and Shrujan Amin and Joseph Ewing and Caroline Rudisill and Alexander McLain
• Companies: University of South Carolina and University of South Carolina and Care Coordination Institute and Prisma Health and University of South Carolina and University of South Carolina
• Keywords: Compartmental Models; Bayesian Statistics; COVID-19; Forecasting

Abstract

During the early months of the COVID-19 pandemic, the concern that the number of COVID-19 patients requiring hospitalization would exceed healthcare system capacity was a primary motivation for lockdowns, non-essential business closures and other forms of social distancing. Uncertainty regarding the demand for COVID-19 inpatient care created an urgent need to accurately predict the number of COVID-19 inpatients at the local level. In this work, we develop a Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered (SIHVR) model to predict the demand for COVID-19 inpatient care at the healthcare system level. The Bayesian SIHVR model provides daily estimates of the number of new COVID-19 patients admitted to inpatient care, the total number of non-ventilated COVID-19 inpatients, and the total number of ventilated COVID-19 patients at the healthcare system level. The model also incorporates county-level data on the number of reported COVID-19 cases and county-level social distancing metrics, making it locally customizable. The model is applied to data from two regional healthcare systems in South Carolina during various stages of the pandemic.