Forecasting severe respiratory disease hospitalizations using machine learning algorithms.

ABSTRACT:

Background

Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on

SUBMITTER: Albrecht S 

PROVIDER: S-EPMC11462891 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature