000095527 001__ 95527
000095527 005__ 20251113150202.0
000095527 0247_ $$2doi$$a10.1371/journal.pcbi.1008035
000095527 0248_ $$2sideral$$a119596
000095527 037__ $$aART-2020-119596
000095527 041__ $$aeng
000095527 100__ $$0(orcid)0000-0002-1192-8707$$aAleta, Alberto
000095527 245__ $$aData-driven contact structures: From homogeneous mixing to multilayer networks
000095527 260__ $$c2020
000095527 5060_ $$aAccess copy available to the general public$$fUnrestricted
000095527 5203_ $$aThe modeling of the spreading of communicable diseases has experienced significant advances in the last two decades or so. This has been possible due to the proliferation of data and the development of new methods to gather, mine and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. Here, we bridge a current gap in the mathematical modeling of infectious diseases and develop a framework that allows to account simultaneously for both the connectivity of individuals and the age-structure of the population. We compare different scenarios, namely, i) the homogeneous mixing setting, ii) one in which only the social mixing is taken into account, iii) a setting that considers the connectivity of individuals alone, and finally, iv) a multilayer representation in which both the social mixing and the number of contacts are included in the model. We analytically show that the thresholds obtained for these four scenarios are different. In addition, we conduct extensive numerical simulations and conclude that heterogeneities in the contact network are important for a proper determination of the epidemic threshold, whereas the age-structure plays a bigger role beyond the onset of the outbreak. Altogether, when it comes to evaluate interventions such as vaccination, both sources of individual heterogeneity are important and should be concurrently considered. Our results also provide an indication of the errors incurred in situations in which one cannot access all needed information in terms of connectivity and age of the population.
000095527 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E36-17R$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/FIS2017-87519-P
000095527 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000095527 590__ $$a4.475$$b2020
000095527 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b8 / 58 = 0.138$$c2020$$dQ1$$eT1
000095527 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b16 / 77 = 0.208$$c2020$$dQ1$$eT1
000095527 592__ $$a2.628$$b2020
000095527 593__ $$aCellular and Molecular Neuroscience$$c2020$$dQ1
000095527 593__ $$aComputational Theory and Mathematics$$c2020$$dQ1
000095527 593__ $$aEcology$$c2020$$dQ1
000095527 593__ $$aMolecular Biology$$c2020$$dQ1
000095527 593__ $$aGenetics$$c2020$$dQ1
000095527 593__ $$aModeling and Simulation$$c2020$$dQ1
000095527 593__ $$aEcology, Evolution, Behavior and Systematics$$c2020$$dQ1
000095527 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000095527 700__ $$aFerraz de Arruda, G.
000095527 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000095527 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000095527 773__ $$g16, 7 (2020), e1008035 [16 pp.]$$pPLoS Comput. Biol.$$tPLOS COMPUTATIONAL BIOLOGY$$x1553-734X
000095527 8564_ $$s1306297$$uhttps://zaguan.unizar.es/record/95527/files/texto_completo.pdf$$yVersión publicada
000095527 8564_ $$s471860$$uhttps://zaguan.unizar.es/record/95527/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000095527 909CO $$ooai:zaguan.unizar.es:95527$$particulos$$pdriver
000095527 951__ $$a2025-11-13-15:00:37
000095527 980__ $$aARTICLE