CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy
dc.contributor.author | Fenga, L | |
dc.date.accessioned | 2022-05-17T08:41:33Z | |
dc.date.issued | 2021-03-04 | |
dc.date.updated | 2022-05-16T17:13:17Z | |
dc.description.abstract | To 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.extent | e10819- | |
dc.identifier.citation | Vol. 9, article e10819 | en_GB |
dc.identifier.doi | https://doi.org/10.7717/peerj.10819 | |
dc.identifier.uri | http://hdl.handle.net/10871/129652 | |
dc.identifier | ORCID: 0000-0002-8185-2680 (Fenga, Livio) | |
dc.language.iso | en | en_GB |
dc.publisher | PeerJ | en_GB |
dc.relation.url | https://github.com/pcm-dpc/COVID-19/tree/master/dati-regioni | en_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.subject | Autoregressive metric | en_GB |
dc.subject | Covid-19 | en_GB |
dc.subject | Maximum entropy bootstrap | en_GB |
dc.subject | Model uncertainty | en_GB |
dc.subject | Number of Italian people infected | en_GB |
dc.title | CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-05-17T08:41:33Z | |
dc.identifier.issn | 2167-8359 | |
exeter.article-number | e10819 | |
dc.description | This is the final version. Available from PeerJ via the DOI in this record. | en_GB |
dc.description | Data 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-regioni | en_GB |
dc.identifier.journal | PeerJ – the Journal of Life & Environmental Sciences | en_GB |
dc.relation.ispartof | PeerJ, 9 | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2021-01-02 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2021-03-04 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-05-17T08:35:07Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2022-05-17T08:41:46Z | |
refterms.panel | C | en_GB |
refterms.dateFirstOnline | 2021-03-04 |
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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.