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dc.contributor.authorCanga Garcés, Atsegine
dc.contributor.authorBidegain Cancer, Gorka
dc.date.accessioned2022-05-23T11:53:18Z
dc.date.available2022-05-23T11:53:18Z
dc.date.issued2022-03-31
dc.identifier.citationGlobal health, epidemiology and genomics 2022 : (2022) // Article ID 244953es_ES
dc.identifier.issn2054-4200
dc.identifier.urihttp://hdl.handle.net/10810/56676
dc.description.abstractSince December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly from Wuhan (China) across the globe, affecting more than 200 countries by mid-2021, with over 190M reported cases and around 4M fatalities. During the first year of the pandemic, affected countries implemented a variety of nonpharmaceutical interventions to control virus transmission. In December 2020, countries started administering several authorised vaccines under a limited supply scenario. In this context, the aim of this study was to develop a SEIR-type continuous-time deterministic disease model, to determine the impact of interaction between different vaccination scenarios and levels of protection measures on disease incidence. For this, the model incorporates (i) a protection measure including low (self-protection), medium (mobility limitation), high (closure of indoor facilities), and very high (lockdown) protection levels, (ii) quarantine for confirmed cases, and (iii) vaccination rate and efficacy of four types of vaccines (Pfizer, Moderna, Astra Zeneca, and Janssen). The model was verified and evaluated using the response timeline and vaccination strategies and rates in the Basque Country (N. Spain). Once the model performance was validated, different initial phase (when 30% of the population is vaccinated) vaccination scenarios were simulated, including (i) a realistic vaccine limited supply scenario and (ii) four potential full vaccine supply scenarios where a unique vaccine type is administered. Significant differences in disease prevalence and cumulative mortality were found between vaccination scenarios for low and medium-level protection measures. For high-level protection measures, any vaccine scenario is effective at limiting the virus transmission and disease mortality. The results obtained here may vary in further studies since there may be some unpredictable factors/covariates. With this in mind, the model here could be easily applied to other regions or countries, modifying the strategies implemented and initial conditions.es_ES
dc.language.isoenges_ES
dc.publisherCambridge University Presses_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectCOVID-19es_ES
dc.subjectcommunicable disease controles_ES
dc.subjecthumanses_ES
dc.subjectincidencees_ES
dc.titleModelling the Effect of the Interaction between Vaccination and Nonpharmaceutical Measures on COVID-19 Incidencees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder2022 Atsegine Canga and Gorka Bidegain. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www.hindawi.com/journals/gheg/2022/9244953/es_ES
dc.identifier.doi10.1155/2022/9244953
dc.departamentoesMatemática aplicadaes_ES
dc.departamentoeuMatematika aplikatuaes_ES


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2022 Atsegine Canga and Gorka Bidegain. )is is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Except where otherwise noted, this item's license is described as 2022 Atsegine Canga and Gorka Bidegain. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.