Please use this identifier to cite or link to this item: http://hdl.handle.net/10362/133057
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dc.contributor.authorCaetano, Constantino P.-
dc.contributor.authorMorgado, Maria Luísa-
dc.contributor.authorPatrício, Paula Cristiana Costa Garcia da Silva-
dc.contributor.authorPereira, João F.-
dc.contributor.authorNunes, Baltazar-
dc.date.accessioned2022-02-16T23:19:32Z-
dc.date.available2022-02-16T23:19:32Z-
dc.date.issued2021-05-12-
dc.identifier.citationCaetano, C. P., Morgado, M. L., Patrício, P. C. C. G. D. S., Pereira, J. F., & Nunes, B. (2021). Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal. Mathematics, 9(10). https://doi.org/10.3390/math9101084-
dc.identifier.otherPURE: 36735227-
dc.identifier.otherPURE UUID: 7f068651-c89d-47e8-bea0-eb992ed69754-
dc.identifier.otherScopus: 85108179769-
dc.identifier.otherWOS: 000655044500001-
dc.identifier.urihttp://hdl.handle.net/10362/133057-
dc.description"DOCTORATES 4 COVID-19", number 2020.10172.BD. UIDB/04621/2020 UIDP/04621/2020 UIDB/00297/2020-
dc.description.abstractIn this paper, we present an age-structured SEIR model that uses contact patterns to reflect the physical distance measures implemented in Portugal to control the COVID-19 pandemic. By using these matrices and proper estimates for the parameters in the model, we were able to ascertain the impact of mitigation strategies employed in the past. Results show that the March 2020 lockdown had an impact on disease transmission, bringing the effective reproduction number (R(t)) below 1. We estimate that there was an increase in the transmission after the initial lift of the measures on 6 May 2020 that resulted in a second wave that was curbed by the October and November measures. December 2020 saw an increase in the transmission reaching an R(t) = 1.45 in early January 2021. Simulations indicate that the lockdown imposed on the 15 January 2021 might reduce the intensive care unit (ICU) demand to below 200 cases in early April if it lasts at least 2 months. As it stands, the model was capable of projecting the number of individuals in each infection phase for each age group and moment in time.en
dc.language.isoeng-
dc.rightsopenAccess-
dc.subjectEpidemic models-
dc.subjectSEIR type compartmental model-
dc.subjectCOVID-19-
dc.subjectmathematical modelling-
dc.subjectcontact matrices-
dc.titleMathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal-
dc.typearticle-
degois.publication.issue10-
degois.publication.titleMathematics-
degois.publication.volume9-
dc.peerreviewedyes-
dc.identifier.doihttps://doi.org/10.3390/math9101084-
dc.description.versionpublishersversion-
dc.description.versionpublished-
dc.contributor.institutionDM - Departamento de Matemática-
dc.contributor.institutionCMA - Centro de Matemática e Aplicações-
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - Pólo ENSP-
dc.contributor.institutionCentro de Investigação em Saúde Pública (CISP/PHRC)-
dc.contributor.institutionEscola Nacional de Saúde Pública (ENSP)-
Appears in Collections:FCT: DM - Artigos em revista internacional com arbitragem científica

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