Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID19 pandemic

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 SARSCoV-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 MayeDecember 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0e1.2.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.