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

Leal, Maria da Conceição Dias; Morgado, Leonel; Oliveira, Teresa A.

Publication

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

2023-02-26Journal article

dc.contributor.author	Leal, Maria da Conceição Dias
dc.contributor.author	Morgado, Leonel
dc.contributor.author	Oliveira, Teresa A.
dc.date.accessioned	2023-02-27T15:20:15Z
dc.date.available	2023-02-27T15:20:15Z
dc.date.issued	2023-02-26
dc.date.updated	2023-02-26T19:30:08Z
dc.description.abstract	During 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.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.doi	10.3390/math11051156	pt_PT
dc.identifier.issn	2227-7390
dc.identifier.slug	cv-prod-3150287
dc.identifier.uri	http://hdl.handle.net/10400.2/13458
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.relation.publisherversion	https://www.mdpi.com/2227-7390/11/5/1156	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Time series segmentation	pt_PT
dc.subject	Modelling COVID-19	pt_PT
dc.subject	Heterogeneous impacts	pt_PT
dc.title	Mathematical and statistical modelling for assessing COVID-19 superspreader contagion: analysis of geographical heterogeneous impacts from public events	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.issue	5	pt_PT
oaire.citation.startPage	1156	pt_PT
oaire.citation.title	Mathematics	pt_PT
oaire.citation.volume	11	pt_PT
person.familyName	Leal
person.familyName	Morgado
person.familyName	Oliveira
person.givenName	Maria da Conceição Dias
person.givenName	Leonel
person.givenName	Teresa A.
person.identifier	2680983
person.identifier	R-000-93D
person.identifier	1155497
person.identifier.ciencia-id	D411-9E54-8EF9
person.identifier.ciencia-id	7119-981F-18A9
person.identifier.ciencia-id	8814-A54B-12DE
person.identifier.orcid	0000-0003-3803-6204
person.identifier.orcid	0000-0001-5517-644X
person.identifier.orcid	0000-0003-3283-9946
person.identifier.rid	F-2692-2010
person.identifier.rid	J-3077-2019
person.identifier.scopus-author-id	56790444100
person.identifier.scopus-author-id	23025615500
person.identifier.scopus-author-id	54403540300
rcaap.cv.cienciaid	7119-981F-18A9 \| LEONEL CASEIRO MORGADO
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
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