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：由奥密克戎亚谱系引发的新冠住院病例持续对英格兰医疗系统造成压力。了解预期医疗需求有助于公共卫生部门制定更有效、及时的规划。我们整合了包括在线搜索数据、NHS 111电话及在线分诊系统在内的症状监测数据源。基于这些数据，我们探索了广义加性模型、广义线性混合模型、惩罚广义线性模型及模型集成方法，在NHS信托层级进行两周预测窗口的预报。此外，我们展示了通过均值集成、加权集成及回归集成等模型组合方式如何提升预测评分。经多波奥密克戎疫情在多个空间尺度验证，我们证实领先指标可提升预测模型性能，尤其在流行病变化节点处。采用多种评分规则的结果表明，集成模型在所有单一模型中表现最优，其21天预测窗口的性能优于对应单一模型的14天预测效果。我们介绍了2022年英格兰公共卫生官员用于指导NHS医疗战略与政策决策的建模框架。本文探讨了集成方法对提升预测性能的重要性，以及新型症状监测在流行病预测中的实际应用价值。