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dc.contributorUniversitat Ramon Llull. IQS
dc.contributor.authorVillanueva, I.
dc.contributor.authorConesa, D.
dc.contributor.authorLópez Cano, C.
dc.contributor.authorPerramon Malavez, Aida
dc.contributor.authorMolinuevo, D.
dc.contributor.authorRioja, V. L. de
dc.contributor.authorLópez, D.
dc.contributor.authorAlonso, Sergio
dc.contributor.authorCardona, J.
dc.contributor.authorMontañola i Sales, Cristina
dc.contributor.authorPrats, C.
dc.contributor.authorAlvarez Lacalle, Enrique
dc.date.accessioned2025-02-24T09:11:02Z
dc.date.available2025-02-24T09:11:02Z
dc.date.issued2024-05-11
dc.identifier.issn2045-2322ca
dc.identifier.urihttp://hdl.handle.net/20.500.14342/5001
dc.description.abstractAccurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two–three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1–2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.ca
dc.format.extent14 p.ca
dc.language.isoengca
dc.publisherNature Researchca
dc.relation.ispartofScientific Reports. 2024;14:10775ca
dc.rights© L'autor/aca
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherComputational modelsca
dc.subject.otherEpidemiologyca
dc.titleCountry-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz modelca
dc.typeinfo:eu-repo/semantics/articleca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.subject.udc616ca
dc.identifier.doihttps://doi.org/10.1038/s41598-024-61233-wca
dc.relation.projectIDinfo:eu-repo/grantAgreement/DEC/SGR/2021 SGR 00582ca
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI-MCIN/PN I+D/PID-2022-139216NB-I00ca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca


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