Who can go back to work when the COVID-19 pandemic remits?
Identifieur interne : 000464 ( Main/Corpus ); précédent : 000463; suivant : 000465Who can go back to work when the COVID-19 pandemic remits?
Auteurs : Luis Angel Hierro ; David Cantarero ; David Pati O ; Daniel Rodríguez-Pérez De ArenazaSource :
- PloS one [ 1932-6203 ] ; 2020.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Betacoronavirus (MeSH), Child (MeSH), Child, Preschool (MeSH), Coronavirus Infections (economics), Coronavirus Infections (epidemiology), Coronavirus Infections (mortality), Decision Making (MeSH), Decision Trees (MeSH), Humans (MeSH), Infant (MeSH), Infant, Newborn (MeSH), Middle Aged (MeSH), Pandemics (economics), Pneumonia, Viral (economics), Pneumonia, Viral (epidemiology), Pneumonia, Viral (mortality), Return to Work (trends), Spain (MeSH), Young Adult (MeSH).
- MESH :
- geographic : Spain.
- economics : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- trends : Return to Work.
- Adolescent, Adult, Aged, Aged, 80 and over, Betacoronavirus, Child, Child, Preschool, Decision Making, Decision Trees, Humans, Infant, Infant, Newborn, Middle Aged, Young Adult.
Abstract
This paper seeks to determine which workers affected by lockdown measures can return to work when a government decides to apply lockdown exit strategies. This system, which we call Sequential Selective Multidimensional Decision (SSMD), involves deciding sequentially, by geographical areas, sectors of activity, age groups and immunity, which workers can return to work at a given time according to the epidemiological criteria of the country as well as that of a group of reference countries, used as a benchmark, that have suffered a lower level of lockdown de-escalation strategies. We apply SSMD to Spain, based on affiliation to the Social Security system prior to the COVID-19 pandemic, and conclude that 98.37% of the population could be affected. The proposed system makes it possible to accurately identify the target population for serological IgG antibody tests in the work field, as well as those affected by special income replacement measures due to lockdown being maintained over a longer period.
DOI: 10.1371/journal.pone.0238299
PubMed: 32853294
PubMed Central: PMC7451540
Links to Exploration step
pubmed:32853294Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Who can go back to work when the COVID-19 pandemic remits?</title><author><name sortKey="Hierro, Luis Angel" sort="Hierro, Luis Angel" uniqKey="Hierro L" first="Luis Angel" last="Hierro">Luis Angel Hierro</name><affiliation><nlm:affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Cantarero, David" sort="Cantarero, David" uniqKey="Cantarero D" first="David" last="Cantarero">David Cantarero</name><affiliation><nlm:affiliation>Department of Economics, Group of Health Economics and Health Services Management, University of Cantabria-IDIVAL, Santander, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Pati O, David" sort="Pati O, David" uniqKey="Pati O D" first="David" last="Pati O">David Pati O</name><affiliation><nlm:affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Rodriguez Perez De Arenaza, Daniel" sort="Rodriguez Perez De Arenaza, Daniel" uniqKey="Rodriguez Perez De Arenaza D" first="Daniel" last="Rodríguez-Pérez De Arenaza">Daniel Rodríguez-Pérez De Arenaza</name><affiliation><nlm:affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32853294</idno><idno type="pmid">32853294</idno><idno type="doi">10.1371/journal.pone.0238299</idno><idno type="pmc">PMC7451540</idno><idno type="wicri:Area/Main/Corpus">000464</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000464</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Who can go back to work when the COVID-19 pandemic remits?</title><author><name sortKey="Hierro, Luis Angel" sort="Hierro, Luis Angel" uniqKey="Hierro L" first="Luis Angel" last="Hierro">Luis Angel Hierro</name><affiliation><nlm:affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Cantarero, David" sort="Cantarero, David" uniqKey="Cantarero D" first="David" last="Cantarero">David Cantarero</name><affiliation><nlm:affiliation>Department of Economics, Group of Health Economics and Health Services Management, University of Cantabria-IDIVAL, Santander, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Pati O, David" sort="Pati O, David" uniqKey="Pati O D" first="David" last="Pati O">David Pati O</name><affiliation><nlm:affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Rodriguez Perez De Arenaza, Daniel" sort="Rodriguez Perez De Arenaza, Daniel" uniqKey="Rodriguez Perez De Arenaza D" first="Daniel" last="Rodríguez-Pérez De Arenaza">Daniel Rodríguez-Pérez De Arenaza</name><affiliation><nlm:affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent (MeSH)</term><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>Aged, 80 and over (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Child (MeSH)</term><term>Child, Preschool (MeSH)</term><term>Coronavirus Infections (economics)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (mortality)</term><term>Decision Making (MeSH)</term><term>Decision Trees (MeSH)</term><term>Humans (MeSH)</term><term>Infant (MeSH)</term><term>Infant, Newborn (MeSH)</term><term>Middle Aged (MeSH)</term><term>Pandemics (economics)</term><term>Pneumonia, Viral (economics)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (mortality)</term><term>Return to Work (trends)</term><term>Spain (MeSH)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Spain</term></keywords><keywords scheme="MESH" qualifier="economics" xml:lang="en"><term>Coronavirus Infections</term><term>Pandemics</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="trends" xml:lang="en"><term>Return to Work</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Aged</term><term>Aged, 80 and over</term><term>Betacoronavirus</term><term>Child</term><term>Child, Preschool</term><term>Decision Making</term><term>Decision Trees</term><term>Humans</term><term>Infant</term><term>Infant, Newborn</term><term>Middle Aged</term><term>Young Adult</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper seeks to determine which workers affected by lockdown measures can return to work when a government decides to apply lockdown exit strategies. This system, which we call Sequential Selective Multidimensional Decision (SSMD), involves deciding sequentially, by geographical areas, sectors of activity, age groups and immunity, which workers can return to work at a given time according to the epidemiological criteria of the country as well as that of a group of reference countries, used as a benchmark, that have suffered a lower level of lockdown de-escalation strategies. We apply SSMD to Spain, based on affiliation to the Social Security system prior to the COVID-19 pandemic, and conclude that 98.37% of the population could be affected. The proposed system makes it possible to accurately identify the target population for serological IgG antibody tests in the work field, as well as those affected by special income replacement measures due to lockdown being maintained over a longer period.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32853294</PMID><DateCompleted><Year>2020</Year><Month>09</Month><Day>04</Day></DateCompleted><DateRevised><Year>2020</Year><Month>09</Month><Day>04</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><Issue>8</Issue><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS One</ISOAbbreviation></Journal><ArticleTitle>Who can go back to work when the COVID-19 pandemic remits?</ArticleTitle><Pagination><MedlinePgn>e0238299</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0238299</ELocationID><Abstract><AbstractText>This paper seeks to determine which workers affected by lockdown measures can return to work when a government decides to apply lockdown exit strategies. This system, which we call Sequential Selective Multidimensional Decision (SSMD), involves deciding sequentially, by geographical areas, sectors of activity, age groups and immunity, which workers can return to work at a given time according to the epidemiological criteria of the country as well as that of a group of reference countries, used as a benchmark, that have suffered a lower level of lockdown de-escalation strategies. We apply SSMD to Spain, based on affiliation to the Social Security system prior to the COVID-19 pandemic, and conclude that 98.37% of the population could be affected. The proposed system makes it possible to accurately identify the target population for serological IgG antibody tests in the work field, as well as those affected by special income replacement measures due to lockdown being maintained over a longer period.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hierro</LastName><ForeName>Luis Angel</ForeName><Initials>LA</Initials><AffiliationInfo><Affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cantarero</LastName><ForeName>David</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Economics, Group of Health Economics and Health Services Management, University of Cantabria-IDIVAL, Santander, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Patiño</LastName><ForeName>David</ForeName><Initials>D</Initials><Identifier Source="ORCID">0000-0002-3313-561X</Identifier><AffiliationInfo><Affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rodríguez-Pérez de Arenaza</LastName><ForeName>Daniel</ForeName><Initials>D</Initials><Identifier Source="ORCID">0000-0002-8569-0295</Identifier><AffiliationInfo><Affiliation>Department of Economics and Economic History, University of Seville, Seville, Spain.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>27</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000401" MajorTopicYN="N">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003657" MajorTopicYN="N">Decision Making</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003663" MajorTopicYN="N">Decision Trees</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007231" MajorTopicYN="N">Infant, Newborn</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000401" MajorTopicYN="N">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062707" MajorTopicYN="N">Return to Work</DescriptorName><QualifierName UI="Q000639" MajorTopicYN="Y">trends</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013030" MajorTopicYN="N" Type="Geographic">Spain</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><CoiStatement>The authors have declared that no competing interests exist.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>08</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>9</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32853294</ArticleId><ArticleId IdType="doi">10.1371/journal.pone.0238299</ArticleId><ArticleId IdType="pii">PONE-D-20-16646</ArticleId><ArticleId IdType="pmc">PMC7451540</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>J Travel Med. 2020 Mar 13;27(2):</Citation><ArticleIdList><ArticleId 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