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

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

作者机构：RTI InternationalResearch Triangle ParkNCUSA ExplosionBerlinGermany North Carolina Department of Health and Human ServicesRaleighNCUSA North Carolina State UniversityDepartment of Population Health and PathobiologyRaleighNCUSA 

出 版 物：《Infectious Disease Modelling》 (传染病建模（英文）)

年 卷 期：2022年第7卷第1期

页      面：277-285页

学科分类：1007[医学-药学(可授医学、理学学位)] 100705[医学-微生物与生化药学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1001[医学-基础医学(可授医学、理学学位)] 100103[医学-病原生物学] 100401[医学-流行病与卫生统计学] 10[医学] 

基　　金：North Carolina Department of Health and Human Services Contract Centers for Disease Control and Prevention Contract 

主　　题：forecast hospital adapted 

摘      要：Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource *** forecasts are most relevant when they are accurate,made available quickly,and updated *** 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 *** 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 *** 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 *** 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 *** 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.