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Mathematical and statistical modelling for assessing COVID-19 superspreader contagion: analysis of geographical heterogeneous impacts from public events

dc.contributor.authorLeal, Maria da Conceição Dias
dc.contributor.authorMorgado, Leonel
dc.contributor.authorOliveira, Teresa A.
dc.date.accessioned2023-02-27T15:20:15Z
dc.date.available2023-02-27T15:20:15Z
dc.date.issued2023-02-26
dc.date.updated2023-02-26T19:30:08Z
dc.description.abstractDuring a pandemic, public discussion and decision-making may be required in face of limited evidence. Data-grounded analysis can support decision-makers in such contexts, contributing to inform public policies. We present an empirical analysis method based on regression modelling and hypotheses testing to assess events for the possibility of occurrence of superspreading contagion with geographically heterogeneous impacts. We demonstrate the method by evaluating the case of the May 1st, 2020 Demonstration in Lisbon, Portugal, on regional growth patterns of COVID-19 cases. The methodology enabled concluding that the counties associated with the change in the growth pattern were those where likely means of travel to the demonstration were chartered buses or private cars, rather than subway or trains. Consequently, superspreading was likely due to travelling to/from the event, not from participating in it. The method is straightforward, prescribing systematic steps. Its application to events subject to media controversy enables extracting well founded conclusions, contributing to informed public discussion and decision-making, within a short time frame of the event occurring.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/math11051156pt_PT
dc.identifier.issn2227-7390
dc.identifier.slugcv-prod-3150287
dc.identifier.urihttp://hdl.handle.net/10400.2/13458
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2227-7390/11/5/1156pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectTime series segmentationpt_PT
dc.subjectModelling COVID-19pt_PT
dc.subjectHeterogeneous impactspt_PT
dc.titleMathematical and statistical modelling for assessing COVID-19 superspreader contagion: analysis of geographical heterogeneous impacts from public eventspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue5pt_PT
oaire.citation.startPage1156pt_PT
oaire.citation.titleMathematicspt_PT
oaire.citation.volume11pt_PT
person.familyNameLeal
person.familyNameMorgado
person.familyNameOliveira
person.givenNameMaria da Conceição Dias
person.givenNameLeonel
person.givenNameTeresa A.
person.identifier2680983
person.identifierR-000-93D
person.identifier1155497
person.identifier.ciencia-idD411-9E54-8EF9
person.identifier.ciencia-id7119-981F-18A9
person.identifier.ciencia-id8814-A54B-12DE
person.identifier.orcid0000-0003-3803-6204
person.identifier.orcid0000-0001-5517-644X
person.identifier.orcid0000-0003-3283-9946
person.identifier.ridF-2692-2010
person.identifier.ridJ-3077-2019
person.identifier.scopus-author-id56790444100
person.identifier.scopus-author-id23025615500
person.identifier.scopus-author-id54403540300
rcaap.cv.cienciaid7119-981F-18A9 | LEONEL CASEIRO MORGADO
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationf61ad8fd-fa21-4c0b-a381-07b593fdc2b2
relation.isAuthorOfPublication310d3b15-55de-40c7-8977-5becf9010910
relation.isAuthorOfPublication82b3cd70-88cc-4d31-b4b0-4705f8496c67
relation.isAuthorOfPublication.latestForDiscovery310d3b15-55de-40c7-8977-5becf9010910

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