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.

翻译：背景：季节性流感在冬季对医疗系统造成沉重负担，而此时医疗系统已处于高压状态。COVID-19大流行期间的防控措施抑制了季节性流感的传播，使得潜在暴发的时机和规模难以预测。方法：我们开发了一种层次化广义加性模型（GAM），用于英格兰亚区域范围内流感病毒检测阳性住院人数的短期预测。该模型融合了时空样条的多级结构、周季节性模式和空间相关性。采用包括区间分数、覆盖率、偏差和中位绝对误差在内的多项性能指标，对2022/23流感季波的预测性能进行评估，并与自回归积分滑动平均（ARIMA）时间序列模型的性能进行对比。结果：GAM方法在高低层级地理区域及疫情波动的不同阶段（包括转折点）的评分规则中均优于ARIMA模型。GAM在14天预测范围内的误差表现与ARIMA在7天预测范围内的表现相当。研究发现GAM的性能对衡量全国疫情趋势的平滑函数灵活性最为敏感。解释：本研究提出了一种融合层次化、空间与时间组件的流感住院人数短期预测新方法。该模型基于数据驱动，可利用预测时实际可获得的信息进行实用部署，解决了疫情预测方法的关键局限性。该模型已由英国卫生安全局和英格兰国民医疗服务体系在冬季用于医疗运营规划。