Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK

Journal article

Abstract Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospitalisations across SARS-CoV-2 waves in England. This analysis includes an evaluation of internet search volumes from Google Trends, NHS triage calls and online queries, the NHS COVID-19 app, lateral flow devices (LFDs), and the ZOE app. Data sources were analysed for their feasibility as leading indicators using Granger causality, cross-correlation, and dynamic time warping at fine spatial scales. Google Trends and NHS triages consistently temporally led admissions in most locations, with lead times ranging from 5 to 20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 app, and LFD testing, which diminished with spatial resolution, showing cross-correlation of leads between –7 and 7 days. The results indicate that novel surveillance sources can be used effectively to understand the expected healthcare burden within hospital administrative areas though the temporal and spatial heterogeneity of these relationships is a key determinant of their operational public health utility.

Authors: Mellor, J; Overton, CE; Fyles, M; Chawner, L; Baxter, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cambridge University Press
• Journal: Epidemiology & Infection
• Volume: 151
• Pages: e172-e172
• Article number: e172
• Publication date: 2023-09-04
• DOI: 10.1017/s0950268823001449
• EISSN: 1469-4409
• ISSN: 0950-2688
• Language: English
• Keywords: Economics; Medicine; Demography; Cartography; Economic growth; Triage; Telemedicine; Health care; Sociology; Geography; Pandemic; Medical emergency; Pathology; Coronavirus disease 2019 (COVID-19)