  We investigate the use of transfer entropy (TE) as a proxy to detect the
contact patterns of the population in epidemic processes. We first apply the
measure to a classical age-stratified SIR model and observe that the recovered
patterns are consistent with the age-mixing matrix that encodes the interaction
of the population. We then apply the TE analysis to real data from the COVID-19
pandemic in Spain and show that it can provide information on how the behavior
of individuals changed through time. We also demonstrate how the underlying
dynamics of the process allow us to build a coarse-grained representation of
the time series that provides more information than raw time series. The
macro-level representation is a more effective scale for analysis, which is an
interesting result within the context of causal analysis across different
scales. These results open the path for more research on the potential use of
informational approaches to extract retrospective information on how
individuals change and adapt their behavior during a pandemic, which is
essential for devising adequate strategies for an efficient control of the
spreading.
