Show simple item record

dc.contributor.authorPietzonka, Patrick
dc.contributor.authorBrorson, Erik
dc.contributor.authorBankes, William
dc.contributor.authorCates, Michael E
dc.contributor.authorJack, Robert
dc.contributor.authorAdhikari, Ronojoy
dc.date.accessioned2021-11-24T20:23:56Z
dc.date.available2021-11-24T20:23:56Z
dc.date.issued2021
dc.date.submitted2021-03-11
dc.identifier.issn1932-6203
dc.identifier.otherpone-d-21-08131
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331044
dc.description.abstractWe apply Bayesian inference methods to a suite of distinct compartmental models of generalised SEIR type, in which diagnosis and quarantine are included via extra compartments. We investigate the evidence for a change in lethality of COVID-19 in late autumn 2020 in the UK, using age-structured, weekly national aggregate data for cases and mortalities. Models that allow a (step-like or graded) change in infection fatality rate (IFR) have consistently higher model evidence than those without. Moreover, they all infer a close to two-fold increase in IFR. This value lies well above most previously available estimates. However, the same models consistently infer that, most probably, the increase in IFR preceded the time window during which variant B.1.1.7 (alpha) became the dominant strain in the UK. Therefore, according to our models, the caseload and mortality data do not offer unequivocal evidence for higher lethality of a new variant. We compare these results for the UK with similar models for Germany and France, which also show increases in inferred IFR during the same period, despite the even later arrival of new variants in those countries. We argue that while the new variant(s) may be one contributing cause of a large increase in IFR in the UK in autumn 2020, other factors, such as seasonality, or pressure on health services, are likely to also have contributed.
dc.languageen
dc.publisherPublic Library of Science (PLoS)
dc.subjectResearch Article
dc.subjectMedicine and health sciences
dc.subjectPeople and places
dc.subjectEngineering and technology
dc.subjectPhysical sciences
dc.subjectEarth sciences
dc.subjectBiology and life sciences
dc.subjectComputer and information sciences
dc.titleBayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK.
dc.typeArticle
dc.date.updated2021-11-24T20:23:56Z
prism.issueIdentifier11
prism.publicationNamePLoS One
prism.volume16
dc.identifier.doi10.17863/CAM.78488
dcterms.dateAccepted2021-10-10
rioxxterms.versionofrecord10.1371/journal.pone.0258968
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Shaman, Jeffrey
dc.contributor.orcidPietzonka, Patrick [0000-0003-1744-3724]
dc.contributor.orcidJack, Robert [0000-0003-0086-4573]
dc.contributor.orcidAdhikari, Ronojoy [0000-0003-4625-5220]
dc.identifier.eissn1932-6203
pubs.funder-project-idEuropean Research Council (740269)
cam.issuedOnline2021-11-24


Files in this item

Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record