Following the UK Government's Living with COVID-19 Strategy and the end of universal testing, hospital admissions are an increasingly important measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at National Health Service (NHS) Trust, regional and national geographies help health services plan capacity needs and prepare for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospital pressure across successive waves of SARS-CoV-2 incidence in England. This includes an analysis of internet search volume values from Google Trends, NHS triage calls and online queries, the NHS COVID-19 App, lateral flow devices and the ZOE App. Data sources were analysed for their feasibility as leading indicators using linear and non-linear methods; granger causality, cross correlations and dynamic time warping at fine spatial scales. Consistent temporal and spatial relationships were found for some of the leading indicators assessed across resurgent waves of COVID-19. Google Trends and NHS queries consistently led admissions in over 70% of Trusts, with lead times ranging from 5-20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 App, and rapid testing, that diminished with granularity, showing limited autocorrelation of leads between -7 to 7 days. This work shows that novel syndromic surveillance data has utility for understanding the expected hospital burden at fine spatial scales. The analysis shows at low level geographies that some surveillance sources can predict hospital admissions, though care must be taken in relying on the lead times and consistency between waves.

翻译：摘要：随着英国政府实施“与COVID-19共存战略”并终止全民检测，住院病例已成为衡量COVID-19大流行压力的重要指标。理解国家卫生服务（NHS）信托机构、区域及全国地理尺度上住院病例的前瞻性指标，有助于卫生服务部门规划能力需求并应对持续性压力。我们探究了英格兰多波次SARS-CoV-2发病率中医院压力前瞻性指标的时空关系。该研究分析了来自Google Trends的互联网搜索量数据、NHS分诊电话及在线查询、NHS COVID-19应用程序、侧向层析检测设备以及ZOE应用程序的数据源。通过线性和非线性方法（格兰杰因果检验、互相关分析及精细空间尺度下的动态时间规整）评估各数据源作为前瞻性指标的可行性。在COVID-19复发性疫情波次中，部分前瞻性指标呈现一致的时空关联性。Google Trends和NHS查询在超过70%的信托机构中持续领先住院病例，先导时间范围为5-20天；而ZOE应用程序、NHS COVID-19应用程序及快速检测的关联性不一致，且随空间粒度细化而减弱，先导指标的自相关仅限于-7至7天区间。研究表明，新型综合征监测数据在理解精细空间尺度下的预期住院负担方面具有实用性。分析显示，在低层级地理单元中，部分监测来源可预测住院病例，但需谨慎依赖各疫情波次间先导时间与一致性的可靠性。