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dc.contributor.authorGuzmán-Merino, Miguel
dc.contributor.authorDurán, Christian
dc.contributor.authorMarinescu, Maria-Cristina
dc.contributor.authorDelgado-Sanz, Concepcion 
dc.contributor.authorGomez-Barroso, Diana 
dc.contributor.authorCarretero, Jesus
dc.contributor.authorSingh, David E
dc.date.accessioned2022-05-05T11:06:47Z
dc.date.available2022-05-05T11:06:47Z
dc.date.issued2021-12
dc.identifier.citationComput Biol Med. 2021 Dec;139:104938.es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/14270
dc.description.abstractAs long as critical levels of vaccination have not been reached to ensure heard immunity, and new SARS-CoV-2 strains are developing, the only realistic way to reduce the infection speed in a population is to track the infected individuals before they pass on the virus. Testing the population via sampling has shown good results in slowing the epidemic spread. Sampling can be implemented at different times during the epidemic and may be done either per individual or for combined groups of people at a time. The work we present here makes two main contributions. We first extend and refine our scalable agent-based COVID-19 simulator to incorporate an improved socio-demographic model which considers professions, as well as a more realistic population mixing model based on contact matrices per country. These extensions are necessary to develop and test various sampling strategies in a scenario including the 62 largest cities in Spain; this is our second contribution. As part of the evaluation, we also analyze the impact of different parameters, such as testing frequency, quarantine time, percentage of quarantine breakers, or group testing, on sampling efficacy. Our results show that the most effective strategies are pooling, rapid antigen test campaigns, and requiring negative testing for access to public areas. The effectiveness of all these strategies can be greatly increased by reducing the number of contacts for infected individual.es_ES
dc.description.sponsorshipThis work has been supported by the Carlos III Institute of Health under the project grant 2020/00183/001, the project grant BCV-2021-1-0011, of the Spanish Supercomputing Network (RES) and the European Union's Horizon 2020 JTI-EuroHPC research and innovation program under grant agreement No 956748. The role of all study sponsors was limited to financial support and did not imply participation of any kind in the study and collection, analysis, and interpretation of data, nor in the writing of the manuscript.es_ES
dc.language.isoenges_ES
dc.publisherElsevier es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAgent-based simulationes_ES
dc.subjectContact matriceses_ES
dc.subjectSARS-CoV-2(COVID-19)es_ES
dc.subjectSampling strategieses_ES
dc.subjectSocial modeles_ES
dc.titleAssessing population-sampling strategies for reducing the COVID-19 incidencees_ES
dc.typejournal articlees_ES
dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.pubmedID34678482es_ES
dc.format.volume139es_ES
dc.format.page104938es_ES
dc.identifier.doi10.1016/j.compbiomed.2021.104938es_ES
dc.contributor.funderRed Española de Supercomputaciónes_ES
dc.contributor.funderUnión Europea. Comisión Europea. H2020 es_ES
dc.contributor.funderInstituto de Salud Carlos III es_ES
dc.description.peerreviewedes_ES
dc.identifier.e-issn1879-0534es_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.compbiomed.2021.104938es_ES
dc.identifier.journalComputers in Biology and Medicinees_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/956748/EUes_ES
dc.rights.accessRightsopen accesses_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/2020/00183/001es_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Este Item está sujeto a una licencia Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 Internacional