COVID-19 Global Risk: Expectation vs. Reality.
Identifieur interne : 000887 ( Main/Exploration ); précédent : 000886; suivant : 000888COVID-19 Global Risk: Expectation vs. Reality.
Auteurs : Mudassar Arsalan [Australie] ; Omar Mubin [Australie] ; Fady Alnajjar ; Belal Alsinglawi [Australie]Source :
- International journal of environmental research and public health [ 1660-4601 ] ; 2020.
Descripteurs français
- KwdFr :
- Betacoronavirus (isolement et purification), Facteurs de risque (MeSH), Géographie (MeSH), Humains (MeSH), Infections à coronavirus (virologie), Infections à coronavirus (épidémiologie), Motivation (MeSH), Pandémies (MeSH), Pneumopathie virale (virologie), Pneumopathie virale (épidémiologie), Sujet âgé (MeSH).
- MESH :
- isolement et purification : Betacoronavirus.
- virologie : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Facteurs de risque, Géographie, Humains, Motivation, Pandémies, Sujet âgé.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- isolation & purification : Betacoronavirus.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Aged, Geography, Humans, Motivation, Pandemics, Risk Factors.
Abstract
Background and Objective: COVID-19 has engulfed the entire world, with many countries struggling to contain the pandemic. In order to understand how each country is impacted by the virus compared with what would have been expected prior to the pandemic and the mortality risk on a global scale, a multi-factor weighted spatial analysis is presented. Method: A number of key developmental indicators across three main categories of demographics, economy, and health infrastructure were used, supplemented with a range of dynamic indicators associated with COVID-19 as independent variables. Using normalised COVID-19 mortality on 13 May 2020 as a dependent variable, a linear regression (N = 153 countries) was performed to assess the predictive power of the various indicators. Results: The results of the assessment show that when in combination, dynamic and static indicators have higher predictive power to explain risk variation in COVID-19 mortality compared with static indicators alone. Furthermore, as of 13 May 2020 most countries were at a similar or lower risk level than what would have been expected pre-COVID, with only 44/153 countries experiencing a more than 20% increase in mortality risk. The ratio of elderly emerges as a strong predictor but it would be worthwhile to consider it in light of the family makeup of individual countries. Conclusion: In conclusion, future avenues of data acquisition related to COVID-19 are suggested. The paper concludes by discussing the ability of various factors to explain COVID-19 mortality risk. The ratio of elderly in combination with the dynamic variables associated with COVID-19 emerge as more significant risk predictors in comparison to socio-economic and demographic indicators.
DOI: 10.3390/ijerph17155592
PubMed: 32756513
PubMed Central: PMC7432363
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">COVID-19 Global Risk: Expectation vs. Reality.</title><author><name sortKey="Arsalan, Mudassar" sort="Arsalan, Mudassar" uniqKey="Arsalan M" first="Mudassar" last="Arsalan">Mudassar Arsalan</name><affiliation wicri:level="1"><nlm:affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116</wicri:regionArea><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Mubin, Omar" sort="Mubin, Omar" uniqKey="Mubin O" first="Omar" last="Mubin">Omar Mubin</name><affiliation wicri:level="1"><nlm:affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116</wicri:regionArea><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Alnajjar, Fady" sort="Alnajjar, Fady" uniqKey="Alnajjar F" first="Fady" last="Alnajjar">Fady Alnajjar</name><affiliation><nlm:affiliation>College of Information Technology, UAE University, Al-Ain, UAE.</nlm:affiliation><wicri:noCountry code="subField">UAE</wicri:noCountry></affiliation></author><author><name sortKey="Alsinglawi, Belal" sort="Alsinglawi, Belal" uniqKey="Alsinglawi B" first="Belal" last="Alsinglawi">Belal Alsinglawi</name><affiliation wicri:level="1"><nlm:affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116</wicri:regionArea><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32756513</idno><idno type="pmid">32756513</idno><idno type="doi">10.3390/ijerph17155592</idno><idno type="pmc">PMC7432363</idno><idno type="wicri:Area/Main/Corpus">000809</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000809</idno><idno type="wicri:Area/Main/Curation">000809</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000809</idno><idno type="wicri:Area/Main/Exploration">000809</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">COVID-19 Global Risk: Expectation vs. Reality.</title><author><name sortKey="Arsalan, Mudassar" sort="Arsalan, Mudassar" uniqKey="Arsalan M" first="Mudassar" last="Arsalan">Mudassar Arsalan</name><affiliation wicri:level="1"><nlm:affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116</wicri:regionArea><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Mubin, Omar" sort="Mubin, Omar" uniqKey="Mubin O" first="Omar" last="Mubin">Omar Mubin</name><affiliation wicri:level="1"><nlm:affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116</wicri:regionArea><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author><author><name sortKey="Alnajjar, Fady" sort="Alnajjar, Fady" uniqKey="Alnajjar F" first="Fady" last="Alnajjar">Fady Alnajjar</name><affiliation><nlm:affiliation>College of Information Technology, UAE University, Al-Ain, UAE.</nlm:affiliation><wicri:noCountry code="subField">UAE</wicri:noCountry></affiliation></author><author><name sortKey="Alsinglawi, Belal" sort="Alsinglawi, Belal" uniqKey="Alsinglawi B" first="Belal" last="Alsinglawi">Belal Alsinglawi</name><affiliation wicri:level="1"><nlm:affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116</wicri:regionArea><placeName><settlement type="city">Sydney</settlement><region type="état">Nouvelle-Galles du Sud</region></placeName></affiliation></author></analytic><series><title level="j">International journal of environmental research and public health</title><idno type="eISSN">1660-4601</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged (MeSH)</term><term>Betacoronavirus (isolation & purification)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (virology)</term><term>Geography (MeSH)</term><term>Humans (MeSH)</term><term>Motivation (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (virology)</term><term>Risk Factors (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Betacoronavirus (isolement et purification)</term><term>Facteurs de risque (MeSH)</term><term>Géographie (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (virologie)</term><term>Infections à coronavirus (épidémiologie)</term><term>Motivation (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (virologie)</term><term>Pneumopathie virale (épidémiologie)</term><term>Sujet âgé (MeSH)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Betacoronavirus</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Betacoronavirus</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Geography</term><term>Humans</term><term>Motivation</term><term>Pandemics</term><term>Risk Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Facteurs de risque</term><term>Géographie</term><term>Humains</term><term>Motivation</term><term>Pandémies</term><term>Sujet âgé</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><i>Background and Objective</i>: COVID-19 has engulfed the entire world, with many countries struggling to contain the pandemic. In order to understand how each country is impacted by the virus compared with what would have been expected prior to the pandemic and the mortality risk on a global scale, a multi-factor weighted spatial analysis is presented. <i>Method</i>: A number of key developmental indicators across three main categories of demographics, economy, and health infrastructure were used, supplemented with a range of dynamic indicators associated with COVID-19 as independent variables. Using normalised COVID-19 mortality on 13 May 2020 as a dependent variable, a linear regression (N = 153 countries) was performed to assess the predictive power of the various indicators. <i>Results</i>: The results of the assessment show that when in combination, dynamic and static indicators have higher predictive power to explain risk variation in COVID-19 mortality compared with static indicators alone. Furthermore, as of 13 May 2020 most countries were at a similar or lower risk level than what would have been expected pre-COVID, with only 44/153 countries experiencing a more than 20% increase in mortality risk. The ratio of elderly emerges as a strong predictor but it would be worthwhile to consider it in light of the family makeup of individual countries. <i>Conclusion</i>: In conclusion, future avenues of data acquisition related to COVID-19 are suggested. The paper concludes by discussing the ability of various factors to explain COVID-19 mortality risk. The ratio of elderly in combination with the dynamic variables associated with COVID-19 emerge as more significant risk predictors in comparison to socio-economic and demographic indicators.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32756513</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>21</Day></DateCompleted><DateRevised><Year>2020</Year><Month>08</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1660-4601</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>15</Issue><PubDate><Year>2020</Year><Month>Aug</Month><Day>03</Day></PubDate></JournalIssue><Title>International journal of environmental research and public health</Title><ISOAbbreviation>Int J Environ Res Public Health</ISOAbbreviation></Journal><ArticleTitle>COVID-19 Global Risk: Expectation vs. Reality.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">E5592</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/ijerph17155592</ELocationID><Abstract><AbstractText><i>Background and Objective</i>: COVID-19 has engulfed the entire world, with many countries struggling to contain the pandemic. In order to understand how each country is impacted by the virus compared with what would have been expected prior to the pandemic and the mortality risk on a global scale, a multi-factor weighted spatial analysis is presented. <i>Method</i>: A number of key developmental indicators across three main categories of demographics, economy, and health infrastructure were used, supplemented with a range of dynamic indicators associated with COVID-19 as independent variables. Using normalised COVID-19 mortality on 13 May 2020 as a dependent variable, a linear regression (N = 153 countries) was performed to assess the predictive power of the various indicators. <i>Results</i>: The results of the assessment show that when in combination, dynamic and static indicators have higher predictive power to explain risk variation in COVID-19 mortality compared with static indicators alone. Furthermore, as of 13 May 2020 most countries were at a similar or lower risk level than what would have been expected pre-COVID, with only 44/153 countries experiencing a more than 20% increase in mortality risk. The ratio of elderly emerges as a strong predictor but it would be worthwhile to consider it in light of the family makeup of individual countries. <i>Conclusion</i>: In conclusion, future avenues of data acquisition related to COVID-19 are suggested. The paper concludes by discussing the ability of various factors to explain COVID-19 mortality risk. The ratio of elderly in combination with the dynamic variables associated with COVID-19 emerge as more significant risk predictors in comparison to socio-economic and demographic indicators.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Arsalan</LastName><ForeName>Mudassar</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0001-9622-5930</Identifier><AffiliationInfo><Affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mubin</LastName><ForeName>Omar</ForeName><Initials>O</Initials><AffiliationInfo><Affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alnajjar</LastName><ForeName>Fady</ForeName><Initials>F</Initials><Identifier Source="ORCID">0000-0001-6102-3765</Identifier><AffiliationInfo><Affiliation>College of Information Technology, UAE University, Al-Ain, UAE.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Alsinglawi</LastName><ForeName>Belal</ForeName><Initials>B</Initials><Identifier Source="ORCID">0000-0003-0316-3641</Identifier><AffiliationInfo><Affiliation>School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2116, Australia.</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>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Int J Environ Res Public Health</MedlineTA><NlmUniqueID>101238455</NlmUniqueID><ISSNLinking>1660-4601</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="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="Y">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005843" MajorTopicYN="N">Geography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009042" MajorTopicYN="N">Motivation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">multi-weighted factor analysis</Keyword><Keyword MajorTopicYN="N">risk evaluation</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>08</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>07</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32756513</ArticleId><ArticleId IdType="pii">ijerph17155592</ArticleId><ArticleId IdType="doi">10.3390/ijerph17155592</ArticleId><ArticleId IdType="pmc">PMC7432363</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Nature. 2008 Feb 21;451(7181):990-3</Citation><ArticleIdList><ArticleId IdType="pubmed">18288193</ArticleId></ArticleIdList></Reference><Reference><Citation>Curr Top Microbiol Immunol. 2014;385:119-36</Citation><ArticleIdList><ArticleId IdType="pubmed">25085014</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 Feb 20;382(8):727-733</Citation><ArticleIdList><ArticleId IdType="pubmed">31978945</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Mar 21;395(10228):931-934</Citation><ArticleIdList><ArticleId IdType="pubmed">32164834</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Apr 4;395(10230):1089</Citation><ArticleIdList><ArticleId IdType="pubmed">32247378</ArticleId></ArticleIdList></Reference><Reference><Citation>J Travel Med. 2020 May 18;27(3):</Citation><ArticleIdList><ArticleId IdType="pubmed">32227186</ArticleId></ArticleIdList></Reference><Reference><Citation>Emerg Infect Dis. 2008 May;14(5):709-15</Citation><ArticleIdList><ArticleId IdType="pubmed">18439350</ArticleId></ArticleIdList></Reference><Reference><Citation>Environ Int. 2011 Feb;37(2):393-403</Citation><ArticleIdList><ArticleId IdType="pubmed">21111481</ArticleId></ArticleIdList></Reference><Reference><Citation>Environ Int. 2012 Sep 15;45:39-43</Citation><ArticleIdList><ArticleId IdType="pubmed">22572115</ArticleId></ArticleIdList></Reference><Reference><Citation>Emerg Infect Dis. 2010 Dec;16(12):1931-7</Citation><ArticleIdList><ArticleId IdType="pubmed">21122224</ArticleId></ArticleIdList></Reference><Reference><Citation>Am Psychol. 2013 May-Jun;68(4):197-209</Citation><ArticleIdList><ArticleId IdType="pubmed">23688088</ArticleId></ArticleIdList></Reference><Reference><Citation>Chaos Solitons Fractals. 2020 Jun;135:109842</Citation><ArticleIdList><ArticleId IdType="pubmed">32341627</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2020 Jul 14;117(28):16118-16120</Citation><ArticleIdList><ArticleId IdType="pubmed">32576696</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2020 May 5;117(18):9696-9698</Citation><ArticleIdList><ArticleId IdType="pubmed">32300018</ArticleId></ArticleIdList></Reference><Reference><Citation>Curr Opin Virol. 2012 Feb;2(1):90-5</Citation><ArticleIdList><ArticleId IdType="pubmed">22440971</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Mar 14;395(10227):871-877</Citation><ArticleIdList><ArticleId IdType="pubmed">32087820</ArticleId></ArticleIdList></Reference><Reference><Citation>Soc Sci Med. 2009 Jun;68(12):2240-6</Citation><ArticleIdList><ArticleId IdType="pubmed">19394122</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2011 Aug 27;378(9793):804-14</Citation><ArticleIdList><ArticleId IdType="pubmed">21872749</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2016 May 12;11(5):e0155044</Citation><ArticleIdList><ArticleId IdType="pubmed">27170999</ArticleId></ArticleIdList></Reference><Reference><Citation>Risk Anal. 2018 Mar;38(3):429-441</Citation><ArticleIdList><ArticleId IdType="pubmed">28810081</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Australie</li></country><region><li>Nouvelle-Galles du Sud</li></region><settlement><li>Sydney</li></settlement></list><tree><noCountry><name sortKey="Alnajjar, Fady" sort="Alnajjar, Fady" uniqKey="Alnajjar F" first="Fady" last="Alnajjar">Fady Alnajjar</name></noCountry><country name="Australie"><region name="Nouvelle-Galles du Sud"><name sortKey="Arsalan, Mudassar" sort="Arsalan, Mudassar" uniqKey="Arsalan M" first="Mudassar" last="Arsalan">Mudassar Arsalan</name></region><name sortKey="Alsinglawi, Belal" sort="Alsinglawi, Belal" uniqKey="Alsinglawi B" first="Belal" last="Alsinglawi">Belal Alsinglawi</name><name sortKey="Mubin, Omar" sort="Mubin, Omar" uniqKey="Mubin O" first="Omar" last="Mubin">Omar Mubin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/CovidSeniorV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000887 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000887 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= CovidSeniorV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= pubmed:32756513
|texte= COVID-19 Global Risk: Expectation vs. Reality.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:32756513" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a CovidSeniorV1
| This area was generated with Dilib version V0.6.37. |  |