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dc.contributor.authorFenga, L
dc.date.accessioned2022-05-17T08:30:49Z
dc.date.issued2021-01-16
dc.date.updated2022-05-16T17:10:25Z
dc.description.abstractThis paper provides a model-based method for the forecast of the total number of currently COVID-19 positive individuals and of the occupancy of the available intensive care units in Italy. The predictions obtained—for a time horizon of 10 days starting from March 29th—will be provided at a national as well as at a more disaggregated level, following a criterion based on the magnitude of the phenomenon. While those regions hit the most by the pandemic have been kept separated, those less affected regions have been aggregated into homogeneous macroareas. Results show that—within the forecast period considered (March 29th–April 7th)—all of the Italian regions will show a decreasing number of COVID-19 positive people. The same will be observed for the number of people who will need to be hospitalized in an intensive care unit. These estimates are valid under constancy of the government’s current containment policies. In this scenario, northern regions will remain the most affected ones, whereas no significant outbreaks are foreseen in the southern regions.en_GB
dc.format.extent1-9
dc.identifier.citationVol. 2021, article 5982784en_GB
dc.identifier.doihttps://doi.org/10.1155/2021/5982784
dc.identifier.urihttp://hdl.handle.net/10871/129650
dc.identifierORCID: 0000-0002-8185-2680 (Fenga, Livio)
dc.language.isoenen_GB
dc.publisherHindawien_GB
dc.rights© 2021 Livio Fenga. This is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_GB
dc.titleForecasting the COVID-19 diffusion in Italy and the related occupancy of intensive care unitsen_GB
dc.typeArticleen_GB
dc.date.available2022-05-17T08:30:49Z
dc.identifier.issn1687-952X
dc.descriptionThis is the final version. Available from Hindawi via the DOI in this record. en_GB
dc.descriptionData Availability: The data used to support the findings of this study are available from the corresponding author upon request.en_GB
dc.identifier.eissn1687-9538
dc.identifier.journalJournal of Probability and Statisticsen_GB
dc.relation.ispartofJournal of Probability and Statistics, 2021
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2020-09-16
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-01-16
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-05-17T08:27:28Z
refterms.versionFCDVoR
refterms.dateFOA2022-05-17T08:31:05Z
refterms.panelCen_GB
refterms.dateFirstOnline2021-01-16


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© 2021 Livio Fenga. This is is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's licence is described as © 2021 Livio Fenga. This is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.