Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic

Alexander Preiss; Emily Hadley; Kasey Jones; Marie C D Stoner; Caroline Kery; Peter Baumgartner; Georgiy Bobashev; Jessica Tenenbaum; Charles Carter; Kimberly Clement; Sarah Rhea

doi:10.1016/j.idm.2022.01.003

Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic

Infect Dis Model. 2022 Mar;7(1):277-285. doi: 10.1016/j.idm.2022.01.003. Epub 2022 Feb 4.

Affiliations

¹ RTI International, Research Triangle Park, NC, USA.
² Explosion, Berlin, Germany.
³ North Carolina Department of Health and Human Services, Raleigh, NC, USA.
⁴ North Carolina State University, Department of Population Health and Pathobiology, Raleigh, NC, USA.

Abstract

Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents' COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May-December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0-1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy.