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dc.contributor.authorGuzmán Merino, Miguel
dc.contributor.authorDurán, Christian
dc.contributor.authorMarinescu, Maria Cristina
dc.contributor.authorDelgado Sanz, Concepción
dc.contributor.authorGómez Barroso, Diana
dc.contributor.authorCarretero Pérez, Jesús
dc.contributor.authorSingh, David E.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2021-11-15T09:14:55Z
dc.date.available2021-11-15T09:14:55Z
dc.date.issued2021-12
dc.identifier.citationGuzmán, M. [et al.]. Assessing population-sampling strategies for reducing the COVID-19 incidence. "Computers in biology and medicine", Desembre 2021, vol. 139, article 104938, p. 1-10.
dc.identifier.issn0010-4825
dc.identifier.urihttp://hdl.handle.net/2117/356397
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.
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.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.lcshCOVID-19 (Disease)
dc.subject.lcshIntelligent agents (Computer software)
dc.subject.lcshCOVID-19 (Disease) -- Computer simulation
dc.subject.otherSARS-CoV-2 (COVID-19)
dc.subject.otherAgent-based simulation
dc.subject.otherSampling strategies
dc.subject.otherSocial model
dc.subject.otherContact matrices
dc.titleAssessing population-sampling strategies for reducing the COVID-19 incidence
dc.typeArticle
dc.subject.lemacCOVID-19 (Malaltia)
dc.subject.lemacAgents intel·ligents (Programari)
dc.subject.lemacCOVID-19 (Malaltia) -- Simulació per ordinador
dc.identifier.doi10.1016/j.compbiomed.2021.104938
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0010482521007320?via%3Dihub
dc.rights.accessOpen Access
local.identifier.drac32218640
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/956748/EU/Adaptive multi-tier intelligent data manager for Exascale/ADMIRE
local.citation.authorGuzmán, M.; Durán, C.; Marinescu, M.; Delgado, C.; Gómez, D.; Carretero, J.; Singh, D.
local.citation.publicationNameComputers in biology and medicine
local.citation.volume139
local.citation.numberarticle 104938
local.citation.startingPage1
local.citation.endingPage10


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