Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models.

ABSTRACT:

Background

Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast.

Methods

We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022

SUBMITTER: Mellor J 

PROVIDER: S-EPMC10733380 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature