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Predicting COVID19 pandemic waves including vaccination data with deep learning

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Predicting COVID19 pandemic waves including vaccination data with deep learning

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dc.contributor.author Begga, Ahmed es_ES
dc.contributor.author Garibo-i-Orts, Óscar es_ES
dc.contributor.author de María-García, Sergi es_ES
dc.contributor.author Escolano, Francisco es_ES
dc.contributor.author Lozano, Miguel A. es_ES
dc.contributor.author Oliver, Nuria es_ES
dc.contributor.author Conejero, J. Alberto es_ES
dc.date.accessioned 2024-05-27T18:07:55Z
dc.date.available 2024-05-27T18:07:55Z
dc.date.issued 2023-12-15 es_ES
dc.identifier.uri http://hdl.handle.net/10251/204434
dc.description.abstract [EN] IntroductionDuring the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear.MethodsWe present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines.ResultsWe empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions.DiscussionWhereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available. es_ES
dc.description.sponsorship The authors have been supported by Valencian Government, Grant VALENCIA IA4COVID (GVA-COVID19/2021/100). The authors also want to thank their previous support by Grants FONDOS SUPERA COVID-19 Santander-CRUE (CD4COVID19 2020- 2021), Fundación BBVA for SARS-CoV-2 research (IA4COVID19 2020-2022), and the Valencian Government, which permitted to initiate this research line. es_ES
dc.language Inglés es_ES
dc.publisher Frontiers Media S.A. es_ES
dc.relation.ispartof Frontiers in Public Health es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject SARS-CoV-2 es_ES
dc.subject COVID-19 es_ES
dc.subject Vaccination es_ES
dc.subject Computational epidemiology es_ES
dc.subject Data science for public health es_ES
dc.subject Recurrent neural networks es_ES
dc.subject Non-pharmaceutical interventions es_ES
dc.subject Pareto-front optimization es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Predicting COVID19 pandemic waves including vaccination data with deep learning es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3389/fpubh.2023.1279364 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GV INNOV.UNI.CIENCIA//GVA-COVID19%2F2021%2F100//VALÈNCIA IA4COVID- Plataforma d¿ajuda en la presa de decisions per a minimitzar l¿impacte econòmic i social de la pandèmia de la covid-19./ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/fBBVA//IA4COVID19 2020-2022/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Matemática Pura y Aplicada - Institut Universitari de Matemàtica Pura i Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Begga, A.; Garibo-I-Orts, Ó.; De María-García, S.; Escolano, F.; Lozano, MA.; Oliver, N.; Conejero, JA. (2023). Predicting COVID19 pandemic waves including vaccination data with deep learning. Frontiers in Public Health. 11. https://doi.org/10.3389/fpubh.2023.1279364 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3389/fpubh.2023.1279364 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.identifier.eissn 2296-2565 es_ES
dc.identifier.pmid 38162619 es_ES
dc.identifier.pmcid PMC10757845 es_ES
dc.relation.pasarela S\505513 es_ES
dc.contributor.funder Banco Santander es_ES
dc.contributor.funder Fundación BBVA es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder Universitat Politècnica de València
dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES
upv.costeAPC 3620 es_ES


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