Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.

Autor:	Reich NG; School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America. Electronic address: nick@umass.edu., Wang Y; School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America., Burns M; Massachusetts Department of Public Health, Boston, MA, United States of America., Ergas R; Massachusetts Department of Public Health, Boston, MA, United States of America., Cramer EY; School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America., Ray EL; School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America.
Jazyk:	angličtina
Zdroj:	Epidemics [Epidemics] 2023 Dec; Vol. 45, pp. 100728. Date of Electronic Publication: 2023 Nov 07.
DOI:	10.1016/j.epidem.2023.100728
Abstrakt:	Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze:	MEDLINE
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