Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic. (May 2021)

• Record Type: Journal Article
• Authors: Fry, Richard; Hollinghurst, Joe; Stagg, Helen R; Thompson, Daniel A; Fronterre, Claudio; Orton, Chris; Lyons, Ronan A; Ford, David V; Sheikh, Aziz; Diggle, Peter J
• Is Part Of: International journal of medical informatics. Volume 149(2021)
• Publication Date: 2021-05
• Subjects: Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2021.104400
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: First high-resolution mapping of COVID-19 prevalence in the UK. Sophisticated geospatial methods to predict community prevalence. Longitudinal change in COVID-19 prevalence. Team science approach using SAIL and the HDR UK network. Abstract: Introduction The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of 'best guess' predictions. Conclusion In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data.