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dc.contributor.authorFenga, L
dc.date.accessioned2022-05-17T08:41:33Z
dc.date.issued2021-03-04
dc.date.updated2022-05-16T17:13:17Z
dc.description.abstractTo date, official data on the number of people infected with the SARS-CoV-2—responsible for the Covid-19—have been released by the Italian Government just on the basis of a non-representative sample of population which tested positive for the swab. However a reliable estimation of the number of infected, including asymptomatic people, turns out to be crucial in the preparation of operational schemes and to estimate the future number of people, who will require, to different extents, medical attentions. In order to overcome the current data shortcoming, this article proposes a bootstrap-driven, estimation procedure for the number of people infected with the SARS-CoV-2. This method is designed to be robust, automatic and suitable to generate estimations at regional level. Obtained results show that, while official data at March the 12th report 12.839 cases in Italy, people infected with the SARS-CoV-2 could be as high as 105.789.en_GB
dc.format.extente10819-
dc.identifier.citationVol. 9, article e10819en_GB
dc.identifier.doihttps://doi.org/10.7717/peerj.10819
dc.identifier.urihttp://hdl.handle.net/10871/129652
dc.identifierORCID: 0000-0002-8185-2680 (Fenga, Livio)
dc.language.isoenen_GB
dc.publisherPeerJen_GB
dc.relation.urlhttps://github.com/pcm-dpc/COVID-19/tree/master/dati-regionien_GB
dc.rights© 2021 L. Fenga. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.en_GB
dc.subjectAutoregressive metricen_GB
dc.subjectCovid-19en_GB
dc.subjectMaximum entropy bootstrapen_GB
dc.subjectModel uncertaintyen_GB
dc.subjectNumber of Italian people infecteden_GB
dc.titleCoViD-19: an automatic, semiparametric estimation method for the population infected in Italyen_GB
dc.typeArticleen_GB
dc.date.available2022-05-17T08:41:33Z
dc.identifier.issn2167-8359
exeter.article-numbere10819
dc.descriptionThis is the final version. Available from PeerJ via the DOI in this record. en_GB
dc.descriptionData Availability: The following information was supplied regarding data availability: Data and code are freely available at GitHub: https://github.com/pcm-dpc/COVID-19/tree/master/dati-regionien_GB
dc.identifier.journalPeerJ – the Journal of Life & Environmental Sciencesen_GB
dc.relation.ispartofPeerJ, 9
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2021-01-02
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-03-04
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-05-17T08:35:07Z
refterms.versionFCDVoR
refterms.dateFOA2022-05-17T08:41:46Z
refterms.panelCen_GB
refterms.dateFirstOnline2021-03-04


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© 2021 L. Fenga. 
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
Except where otherwise noted, this item's licence is described as © 2021 L. Fenga. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.