000076928 001__ 76928 000076928 005__ 20200117221622.0 000076928 0247_ $$2doi$$a10.1371/journal.pcbi.1006638 000076928 0248_ $$2sideral$$a109819 000076928 037__ $$aART-2018-109819 000076928 041__ $$aeng 000076928 100__ $$0(orcid)0000-0003-3259-0933$$aArregui, S.$$uUniversidad de Zaragoza 000076928 245__ $$aProjecting social contact matrices to different demographic structures 000076928 260__ $$c2018 000076928 5060_ $$aAccess copy available to the general public$$fUnrestricted 000076928 5203_ $$aThe modeling of large-scale communicable epidemics has greatly benefited in the last years from the increasing availability of highly detailed data. Particullarly, in order to achieve quantitative descriptions of the evolution of epidemics, contact networks and mixing patterns are key. These heterogeneous patterns depend on several factors such as location, socioeconomic conditions, time, and age. This last factor has been shown to encapsulate a large fraction of the observed inter-individual variation in contact patterns, an observation validated by different measurements of age-dependent contact matrices. Recently, several works have studied how to project those matrices to areas where empirical data are not available. However, the dependence of contact matrices on demographic structures and their time evolution has been largely neglected. In this work, we tackle the problem of how to transform an empirical contact matrix that has been obtained for a given demographic structure into a different contact matrix that is compatible with a different demography. The methodology discussed here allows to extrapolate a contact structure measured in a particular area to any other whose demographic structure is known, as well as to obtain the time evolution of contact matrices as a function of the demographic dynamics of the populations they refer to. To quantify the effect of considering time-dynamics of contact patterns on disease modeling, we implemented a Susceptible-Exposed-Infected-Recovered (SEIR) model on 16 different countries and regions and evaluated the impact of neglecting the temporal evolution of mixing patterns. Our results show that simulated disease incidence rates, both at the aggregated and age-specific levels, are significantly dependent on contact structures variation driven by demographic evolution. The present work opens the path to eliminate technical biases from model-based impact evaluations of future epidemic threats and warns against the use of contact matrices to model diseases without correcting for demographic evolution or geographic variations. 000076928 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/FIS2017-87519-P 000076928 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000076928 592__ $$a2.949$$b2018 000076928 593__ $$aCellular and Molecular Neuroscience$$c2018$$dQ1 000076928 593__ $$aComputational Theory and Mathematics$$c2018$$dQ1 000076928 593__ $$aEcology$$c2018$$dQ1 000076928 593__ $$aMolecular Biology$$c2018$$dQ1 000076928 593__ $$aGenetics$$c2018$$dQ1 000076928 593__ $$aModeling and Simulation$$c2018$$dQ1 000076928 593__ $$aEcology, Evolution, Behavior and Systematics$$c2018$$dQ1 000076928 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000076928 700__ $$0(orcid)0000-0002-1192-8707$$aAleta, A.$$uUniversidad de Zaragoza 000076928 700__ $$aSanz, J. 000076928 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Y.$$uUniversidad de Zaragoza 000076928 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica 000076928 773__ $$g14, 12 (2018), e1006638$$pPLoS Comput. Biol.$$tPLoS computational biology$$x1553-7358 000076928 8564_ $$s846829$$uhttps://zaguan.unizar.es/record/76928/files/texto_completo.pdf$$yVersión publicada 000076928 8564_ $$s103944$$uhttps://zaguan.unizar.es/record/76928/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000076928 909CO $$ooai:zaguan.unizar.es:76928$$particulos$$pdriver 000076928 951__ $$a2020-01-17-21:54:43 000076928 980__ $$aARTICLE