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Addressing hospitalisations with non-error-free data by generalised SEIR modelling of COVID-19 pandemic

2021-12-01Artigo científico Acesso aberto
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dc.contributor.authorMendes, Jorge M.
dc.contributor.authorCoelho, Pedro S.
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblNature Publishing Group
dc.date.accessioned2021-10-13T23:20:10Z
dc.date.available2021-10-13T23:20:10Z
dc.date.issued2021-12-01
dc.descriptionMendes, J. M., & Coelho, P. S. (2021). Addressing hospitalisations with non-error-free data by generalised SEIR modelling of COVID-19 pandemic. Scientific Reports, 11(1), 1-20. [19617]. https://doi.org/10.1038/s41598-021-98975-w
dc.description.abstractSuccessive generalisations of the basic SEIR model have been proposed to accommodate the different needs of the organisations handling the SARS-CoV-2 epidemic. These generalisations have not been able until today to represent the potential of the epidemic to overwhelm hospital capacity until today. This work builds on previous generalisations, including a new compartment for hospital occupancy that allows accounting for the infected patients that need specialised medical attention. Consequently, a deeper understanding of the hospitalisations rate and probability as well as of the recovery rates for hospitalised and non-hospitalised individuals is achieved, offering new information and predictions of crucial importance for the planning of the health systems and global epidemic response. Additionally, a new methodology to calibrate epidemic flows between compartments is proposed. We conclude that the two-step calibration procedure is able to recalibrate non-error-free data and showed crucial to reconstruct the series in a specific situation characterised by significant errors over the official recovery cases. The performed modelling also allowed us to understand how effective the several interventions (lockdown or other mobility restriction measures) were, offering insight for helping public authorities to set the timing and intensity of the measures in order to avoid the implosion of the health systems.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent20
dc.format.extent2349980
dc.identifier.doi10.1038/s41598-021-98975-w
dc.identifier.issn2045-2322
dc.identifier.otherPURE: 34257019
dc.identifier.otherPURE UUID: 62dd2a31-8c01-4886-ae86-5b1bc9613643
dc.identifier.otherPubMed: 34608201
dc.identifier.otherPubMedCentral: PMC8490474
dc.identifier.otherORCID: /0000-0003-0828-9956/work/101442674
dc.identifier.otherScopus: 85116382353
dc.identifier.otherWOS: 000703622500060
dc.identifier.otherORCID: /0000-0003-2251-3803/work/152085036
dc.identifier.urihttp://hdl.handle.net/10362/126081
dc.identifier.urlhttps://www.scopus.com/pages/publications/85116382353
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:000703622500060
dc.language.isoeng
dc.peerreviewedyes
dc.subjectGeneral
dc.subjectSDG 3 - Good Health and Well-being
dc.titleAddressing hospitalisations with non-error-free data by generalised SEIR modelling of COVID-19 pandemicen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue1
degois.publication.lastPage20
degois.publication.titleScientific Reports
degois.publication.volume11
dspace.entity.typePublication
rcaap.rightsopenAccess

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