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We investigate the use of transfer entropy (TE) as a proxy to detect thecontact patterns of the population in epidemic processes. We first apply themeasure to a classical age-stratified SIR model and observe that the recoveredpatterns are consistent with the age-mixing matrix that encodes the interactionof the population. We then apply the TE analysis to real data from the COVID-19pandemic in Spain and show that it can provide information on how the behaviorof individuals changed through time. We also demonstrate how the underlyingdynamics of the process allow us to build a coarse-grained representation ofthe time series that provides more information than raw time series. Themacro-level representation is a more effective scale for analysis, which is aninteresting result within the context of causal analysis across differentscales. These results open the path for more research on the potential use ofinformational approaches to extract retrospective information on howindividuals change and adapt their behavior during a pandemic, which isessential for devising adequate strategies for an efficient control of thespreading.

An informational approach to uncover the age group interactions in epidemic spreading from macro analysis