Digital contact tracing data reveals detailed epidemic patterns in England and Wales

Data from digital contact tracing, using examples from the National Health Service (NHS) COVID-19 app for England and Wales, revealed epidemic dynamics in unprecedented detail. The results show that digital contact tracing can provide information with unprecedented temporal resolution, in addition to its primary goal of reducing transmission. “Digital tools are likely to play an even more important role in the next pandemic, given their unparalleled scalability and the insights they can provide for precision public health,” the study authors write.

Outbreaks are the result of complex and dynamic transmission patterns, making it difficult to measure all aspects of the process. This is particularly true for rapidly spreading respiratory pathogens. Detailed characterization of these systems can inform public health decisions and enable targeted interventions. Digital contact tracing, proposed early in the COVID-19 pandemic, has been evaluated for its effectiveness in reducing transmission, but its potential to improve outbreak surveillance has remained largely underexplored.

Here, Michelle Kendall and colleagues report an analysis of anonymised data from the NHS COVID-19 app for England and Wales, which was in use between 2021 and early 2023. Kendall et al. The app data detected significant changes in contact rates and transmissibility, reflecting events such as the easing of lockdown measures, the Christmas holidays and the Euro 2020 championship. The introduction of vaccines and the emergence of the Delta and Omicron variants also influenced these dynamics. The analysis revealed a shift from household infections to fleeting contacts, particularly on weekends and during holidays.

Notably, transient infections peaked on Saturdays and household infections on Sundays. The app detected 57,000 infections during Euro 2020, highlighting the impact of nationwide gatherings on transmission. Seasonal holidays showed a trend toward increased mixing followed by a quiet period in January. Additionally, the data allowed Kendall et al. develop a new indicator – R_application