Staff-Pupil SARS-CoV-2 Infection Pathways in Schools: A Population Level Linked Data Approach (preprint)

Abstract Background: Better understanding of the role that children and school staff play in the transmission of SARS-CoV-2 is essential to guide policy development on controlling infection whilst minimising disruption to children's education and wellbeing. Methods: Our national e-cohort (n=500,779) study used anonymised linked data for pupils, staff and associated households linked via educational settings. We estimated the risk of testing positive for SARS-CoV-2 infection for staff and pupils over the period August-December 2020, dependent on measures of recent exposure to known cases linked to their educational settings. Results: The total number of cases in a school was not associated with a subsequent increase in the risk of testing positive (Staff OR per case 0.92, 95%CI 0.85, 1.00; Pupils OR per case 0.98, 95%CI 0.93, 1.02). Amongst pupils, the number of recent cases within the same year group was significantly associated with subsequent increased risk of testing positive (OR per case 1.12, 95%CI 1.08 - 1.15). These effects were adjusted for a range of demographic covariates, and in particular any known cases within the same household, which had the strongest association with testing positive (Staff OR 39.86, 95%CI 35.01, 45.38, pupil OR 9.39, 95%CI 8.94 - 9.88). Conclusions: In a national school cohort, the odds of staff testing positive for SARS-CoV-2 infection were not significantly increased in the 14-day period after case detection in the school. However, pupils were found to be at increased risk, following cases appearing within their own year group, where most of their contacts occur. Strong mitigation measures over the whole of the study period may have reduced wider spread within the school environment.

Real-time spatial health surveillance: mapping the UK COVID-19 epidemic (preprint)

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.