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对英国医疗系统造成了持续压力。了解预期医疗需求有助于公共卫生部门更高效、及时地制定规划。我们收集了包括在线搜索数据、NHS 111电话及在线分诊系统在内的症候群监测数据源。结合这些数据，我们探索了广义可加模型、广义线性混合模型、惩罚广义线性模型及模型集成方法，以NHS信托层级为预测单元开展两周预测时域内的预测。此外，我们通过均值集成、加权集成及回归集成展示了模型组合如何提升预测评分。经多个奥密克戎波次及不同空间尺度验证，我们证实领先指标可提升预测模型性能，尤其在流行病转折点处。通过多种评分规则比较，集成方法在所有单一模型中表现最优，其21天窗口预测效能高于对应单一模型在14天窗口的表现。我们介绍了2022年英国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在提升预测性能方面的重要性，以及新型症候群监测在流行病预测中的实践应用路径。