  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 the potential of combined online-offline data collections to
understand the changing behavioural responses determining the future evolution
of the outbreak, and inform epidemic models with crucial data.
