  Background: Seasonal influenza causes a substantial burden on healthcare
services over the winter period when these systems are already under pressure.
Policies during the COVID-19 pandemic supressed the transmission of season
influenza, making the timing and magnitude of a potential resurgence difficult
to predict.
  Methods: We 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 seasonality, and
spatial correlation. Using multiple performance metrics including interval
score, coverage, bias, and median absolute error, the predictive performance is
evaluated for the 2022/23 seasonal wave. Performance is measured against an
autoregressive integrated moving average (ARIMA) time series model.
  Results: The GAM method outperformed the ARIMA model across scoring rules at
both high and low-level geographies, and across the different phases of the
epidemic wave including the turning point. The performance of the GAM with a
14-day forecast horizon was comparable in error to the ARIMA at 7 days. The
performance of the GAM is found to be most sensitive to the flexibility of the
smoothing function that measures the national epidemic trend.
  Interpretation: This study introduces a novel approach to short-term
forecasting of hospital admissions with influenza using hierarchical, spatial,
and temporal components. The model is data-driven and practical to deploy using
information realistically available at time of prediction, addressing key
limitations of epidemic forecasting approaches. This model was used across the
winter for healthcare operational planning by the UK Health Security Agency and
the National Health Service in England.
