Near-real time estimations of the effective reproduction number are among the
most important tools to track the progression of a pandemic and to inform
policy makers and the general public. However, these estimations rely on
reported case numbers, commonly recorded with significant biases. The epidemic
outcome is strongly influenced by the dynamics of social contacts, which are
neglected in conventional surveillance systems as their real-time observation
is challenging. Here, we propose a concept using online and offline behavioral
data, recording age-stratified contact matrices at a daily rate. Modeling the
epidemic using the reconstructed matrices we dynamically estimate the effective
reproduction number during the two first waves of the COVID-19 pandemic in
Hungary. Our results demonstrate how behavioral data can be used to build
alternative monitoring systems complementing the established public health
surveillance. They can identify and provide better signals during periods when
official estimates appear unreliable due to observational biases