The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. 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. A pre-requisite to an effective control strategy is that predictions need to be accompanied by estimates of their precision, to guard against over-reaction to potentially spurious features of ‘best guess’ predictions. 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.
Original content: https://web.www.healthdatagateway.org/paper/7721757921335481
