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Uncertainty and inconsistency of COVID-19 non-pharmaceutical intervention effects with multiple competitive statistical models

2026-01-19Artigo científico Acesso aberto
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dc.contributor.authorMüller, Bernhard
dc.contributor.authorPadberg, Inken
dc.contributor.authorLorke, Michael
dc.contributor.authorBrinks, Ralph
dc.contributor.authorCripps, Sally
dc.contributor.authorGomes, M. Gabriela M.
dc.contributor.authorHaake, Daniel
dc.contributor.authorIoannidis, John P. A.
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.pblNature Publishing Group
dc.date.accessioned2026-03-24T14:17:01Z
dc.date.available2026-03-24T14:17:01Z
dc.date.issued2026-01-19
dc.descriptionPublisher Copyright: © The Author(s) 2026.
dc.description.abstractQuantifying the effect of non-pharmaceutical interventions (NPIs) is essential for formulating lessons from the COVID-19 pandemic. To enable a more reliable and rigorous evaluation of NPIs based on time series data, we reanalyse the official evaluation of NPIs in Germany. As the first part of a multi-step validation and verification project, we focus on properly analysing statistical uncertainties for time series data. Using a set of 9 competitive statistical methods for estimating the effects of NPIs and other determinants of disease spread on the effective reproduction number, we find significantly wider confidence intervals than the official evaluation. In addition to vaccination and seasonality, only few NPIs – such as restrictions in public spaces – can be confidently associated with variations in, but even then effect sizes have large uncertainties. Furthermore, due to multicollinearity in NPI activation patterns, it is difficult to distinguish potential effects of NPIs in public spaces from other interventions that came into force early, such as physical distancing. In future, NPIs should be more carefully designed and accompanied by plans for data collections to allow for a timely evaluation of benefits and harms as a basis for an effective and proportionate response.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent15
dc.format.extent2146330
dc.identifier.doi10.1038/s41598-026-36265-z
dc.identifier.issn2045-2322
dc.identifier.otherPURE: 157116559
dc.identifier.otherPURE UUID: bb0e32bd-ee08-4364-9877-38abe0cbd5b9
dc.identifier.otherScopus: 105029753173
dc.identifier.otherPubMed: 41554897
dc.identifier.otherWOS: 001688972800007
dc.identifier.otherPubMedCentral: PMC12894682
dc.identifier.urihttp://hdl.handle.net/10362/201785
dc.identifier.urlhttps://www.scopus.com/pages/publications/105029753173
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001688972800007
dc.language.isoeng
dc.peerreviewedyes
dc.subjectGeneral
dc.subjectSDG 3 - Good Health and Well-being
dc.titleUncertainty and inconsistency of COVID-19 non-pharmaceutical intervention effects with multiple competitive statistical modelsen
dc.typejournal article
degois.publication.firstPage1
degois.publication.lastPage15
degois.publication.titleScientific Reports
degois.publication.volume16
dspace.entity.typePublication
rcaap.rightsopenAccess

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