cam.issuedOnline	2021-11-24
datacite.contributor.supervisor	editor: Shaman, Jeffrey
dc.contributor.author	Pietzonka, Patrick
dc.contributor.author	Brorson, Erik
dc.contributor.author	Bankes, William
dc.contributor.author	Cates, Michael E
dc.contributor.author	Jack, Robert
dc.contributor.author	Adhikari, Ronojoy
dc.contributor.orcid	Pietzonka, Patrick [0000-0003-1744-3724]
dc.contributor.orcid	Jack, Robert [0000-0003-0086-4573]
dc.contributor.orcid	Adhikari, Ronojoy [0000-0003-4625-5220]
dc.date.accessioned	2021-11-24T20:23:56Z
dc.date.available	2021-11-24T20:23:56Z
dc.date.issued	2021
dc.date.submitted	2021-03-11
dc.date.updated	2021-11-24T20:23:56Z
dc.description.abstract	We 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.identifier.doi	10.17863/CAM.78488
dc.identifier.eissn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.other	pone-d-21-08131
dc.identifier.uri	https://www.repository.cam.ac.uk/handle/1810/331044
dc.language	en
dc.publisher	Public Library of Science (PLoS)
dc.subject	Research Article
dc.subject	Medicine and health sciences
dc.subject	People and places
dc.subject	Engineering and technology
dc.subject	Physical sciences
dc.subject	Earth sciences
dc.subject	Biology and life sciences
dc.subject	Computer and information sciences
dc.title	Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK.
dc.type	Article
dcterms.dateAccepted	2021-10-10
prism.issueIdentifier	11
prism.publicationName	PLoS One
prism.volume	16
pubs.funder-project-id	European Research Council (740269)
rioxxterms.licenseref.uri	http://creativecommons.org/licenses/by/4.0/
rioxxterms.version	VoR
rioxxterms.versionofrecord	10.1371/journal.pone.0258968

Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK.

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