Demography-independent behavioural dynamics influenced the spread of COVID-19 in Denmark
Understanding the factors that impact how a communicable disease like COVID-19 spreads is of central importance to mitigate future outbreaks. Traditionally, epidemic surveillance and forecasting analyses have focused on epidemiological data but recent advancements have demonstrated that monitoring behavioural changes may be equally important. Prior studies have shown that high-frequency survey data on social contact behaviour were able to improve predictions of epidemiological observables during the COVID-19 pandemic. Yet, the full potential of such highly granular survey data remains debated. Here, we utilise daily nationally representative survey data from Denmark collected during 23 months of the COVID-19 pandemic to demonstrate two central use-cases for such high-frequency survey data. First, we show that complex behavioural patterns across demographics collapse to a small number of universal key features, greatly simplifying the monitoring and analysis of adherence to outbreak-mitigation measures. Notably, the temporal evolution of the self-reported median number of face-to-face contacts follows a universal behavioural pattern across age groups, with potential to simplify analysis efforts for future outbreaks. Second, we show that these key features can be leveraged to improve deep-learning-based predictions of daily reported new infections. In particular, our models detect a strong link between aggregated self-reported social distancing and hygiene behaviours and the number of new cases in the subsequent days. Taken together, our results highlight the value of high-frequency surveys to improve our understanding of population behaviour in an ongoing public health crisis and its potential use for prediction of central epidemiological observables.