Hospitalisations from COVID-19 with Omicron sub-lineages have put a sustained pressure on the English healthcare system. Understanding the expected healthcare demand enables more effective and timely planning from public health. We collect syndromic surveillance sources, which include online search data, NHS 111 telephonic and online triages. Incorporating this data we explore generalised additive models, generalised linear mixed-models, penalised generalised linear models and model ensemble methods to forecast over a two-week forecast horizon at an NHS Trust level. Furthermore, we showcase how model combinations improve forecast scoring through a mean ensemble, weighted ensemble, and ensemble by regression. Validated over multiple Omicron waves, at different spatial scales, we show that leading indicators can improve performance of forecasting models, particularly at epidemic changepoints. Using a variety of scoring rules, we show that ensemble approaches outperformed all individual models, providing higher performance at a 21-day window than the corresponding individual models at 14-days. We introduce a modelling structure used by public health officials in England in 2022 to inform NHS healthcare strategy and policy decision making. This paper explores the significance of ensemble methods to improve forecasting performance and how novel syndromic surveillance can be practically applied in epidemic forecasting.

翻译：奥密克戎亚型引发的COVID-19住院病例持续给英格兰医疗系统带来压力。了解预期医疗需求有助于公共卫生部门制定更有效和及时的规划。我们收集了综合症监测数据源，包括在线搜索数据、NHS 111电话和在线分诊系统。结合这些数据，我们探索了广义加性模型、广义线性混合模型、惩罚广义线性模型以及模型集成方法，在NHS信托层面进行为期两周的预测。此外，我们展示了如何通过均值集成、加权集成和回归集成等模型组合来改进预测评分。通过在多个奥密克戎疫情波次及不同空间尺度的验证，我们发现领先指标能够提升预测模型的性能，尤其是在疫情突变点。利用多种评分规则，我们证明集成方法优于所有单一模型，在21天时间窗口内的表现甚至优于对应单一模型在14天窗口内的表现。我们介绍了2022年英格兰公共卫生官员用于指导NHS医疗策略和政策决策的建模框架。本文探讨了集成方法在提升预测性能中的重要性，以及新型综合症监测如何在疫情预测中实际应用。