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Granger causality and time series regression for modelling the migratory dynamics of infuenza into Brazil

dc.contributor.authorGrande, Aline Foersterpt_BR
dc.contributor.authorPumi, Guilhermept_BR
dc.contributor.authorCybis, Gabriela Bettellapt_BR
dc.date.accessioned2024-02-09T05:07:41Zpt_BR
dc.date.issued2023pt_BR
dc.identifier.issn1696-2281pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/271783pt_BR
dc.description.abstractIn this work we study the problem of modelling and forecasting the dynamics of the infuenza virus in Brazil at a given month, from data on reported cases and genetic diversity collected from previous months, in other locations. Granger causality is employed as a tool to assess possible predictive relationships between covariates. For modelling and forecasting purposes, a time series regression approach is applied considering lagged information regarding reported cases and genetic diversity in other regions. Three different models are analysed, including stepwise time series regression and LASSO.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofSORT (Statistics and Operations Research Transactions). spain, Barcelona Institut d'Estadística de Catalunya. Vol.46 (2022), p. 161-188pt_BR
dc.rightsOpen Accessen
dc.subjectGripept_BR
dc.subjectFluen
dc.subjectRegressao estatisticapt_BR
dc.subjectTime series regression,en
dc.subjectSeleção de variáveispt_BR
dc.subjectVariable selectionen
dc.subjectGenetic diversityen
dc.subjectDiversidade genéticapt_BR
dc.subjectCausalidadept_BR
dc.subjectGranger causalityen
dc.titleGranger causality and time series regression for modelling the migratory dynamics of infuenza into Brazilpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001174042pt_BR
dc.type.originEstrangeiropt_BR


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