000148958 001__ 148958
000148958 005__ 20250923084442.0
000148958 0247_ $$2doi$$a10.1103/PhysRevE.110.064321
000148958 0248_ $$2sideral$$a142100
000148958 037__ $$aART-2024-142100
000148958 041__ $$aeng
000148958 100__ $$aMartinelli, Tiago
000148958 245__ $$aInformational approach to uncover the age group interactions in epidemic spreading from macro analysis
000148958 260__ $$c2024
000148958 5203_ $$aIn this study, we explore transfer entropy (TE) as a tool to explore the evolution of population contact patterns in epidemic processes. Initially, we apply TE to a classical age-stratified SIR model and find that the inferred patterns align with the interaction structure of the population, as defined by the age-mixing matrix. Applying this methodology to the COVID-19 pandemic data from Spain, we illustrate how TE can capture temporal changes in individual behavior. Furthermore, we demonstrate that incorporating the inherent dynamics of the epidemic process allows us to create a coarse-grained representation of the time series, providing richer information than raw data. We argue that this macro-level perspective is enhanced by the effectiveness of causal analysis across different scales. Our findings underscore the potential of informational approaches to retrospectively track behavioral adaptations during a pandemic, offering valuable insights for tailoring strategies to control disease spread. This paper paves the way for future research into using such methods for model-free estimation of contact patterns during pandemics.
000148958 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E36-23R-FENOL$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-115800GB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/RYC2021-033226-I
000148958 540__ $$9info:eu-repo/semantics/closedAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000148958 590__ $$a2.4$$b2024
000148958 592__ $$a0.705$$b2024
000148958 591__ $$aPHYSICS, MATHEMATICAL$$b13 / 61 = 0.213$$c2024$$dQ1$$eT1
000148958 593__ $$aCondensed Matter Physics$$c2024$$dQ2
000148958 591__ $$aPHYSICS, FLUIDS & PLASMAS$$b17 / 41 = 0.415$$c2024$$dQ2$$eT2
000148958 593__ $$aStatistics and Probability$$c2024$$dQ2
000148958 593__ $$aStatistical and Nonlinear Physics$$c2024$$dQ2
000148958 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000148958 700__ $$0(orcid)0000-0002-1192-8707$$aAleta, Alberto$$uUniversidad de Zaragoza
000148958 700__ $$aRodrigues, Francisco A.
000148958 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000148958 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000148958 773__ $$g110, 6 (2024), [13 pp.]$$pPhys. rev., E$$tPhysical Review E$$x2470-0045
000148958 8564_ $$s2587229$$uhttps://zaguan.unizar.es/record/148958/files/texto_completo.pdf$$yPostprint
000148958 8564_ $$s526227$$uhttps://zaguan.unizar.es/record/148958/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000148958 909CO $$ooai:zaguan.unizar.es:148958$$particulos$$pdriver
000148958 951__ $$a2025-09-22-14:51:23
000148958 980__ $$aARTICLE