The unprecedented behavioural responses of societies have been evidently shaping the
COVID-19 pandemic, yet it is a significant challenge to accurately monitor the continuously
changing social mixing patterns in real-time. Contact matrices, usually stratified
by age, summarise interaction motifs efficiently, but their collection relies on conventional
representative survey techniques, which are expensive and slow to obtain. Here we
report a data collection effort involving over 2.3% of the Hungarian population to
simultaneously record contact matrices through a longitudinal online and sequence
of representative phone surveys. To correct non-representative biases characterising
the online data, by using census data and the representative samples we develop a
reconstruction method to provide a scalable, cheap, and flexible way to dynamically
obtain closer-to-representative contact matrices. Our results demonstrate that although
some conventional socio-demographic characters correlate significantly with the change
of contact numbers, the strongest predictors can be collected only via surveys techniques
and combined with census data for the best reconstruction performance. We demonstrate
the potential of combined online-offline data collections to understand the changing
behavioural responses determining the future evolution of the outbreak, and to inform
epidemic models with crucial data.
