Impact of complete lockdown on total infection and death rates: A hierarchical cluster analysis.
Identifieur interne : 000055 ( Main/Exploration ); précédent : 000054; suivant : 000056Impact of complete lockdown on total infection and death rates: A hierarchical cluster analysis.
Auteurs : Samit Ghosal [Inde] ; Rahul Bhattacharyya [Inde] ; Milan Majumder [Inde]Source :
- Diabetes & metabolic syndrome [ 1878-0334 ]
Descripteurs français
- KwdFr :
- Analyse de regroupements (MeSH), Betacoronavirus (pathogénicité), Contrôle des maladies transmissibles (méthodes), Humains (MeSH), Infections à coronavirus (mortalité), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Pandémies (prévention et contrôle), Pneumopathie virale (mortalité), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (épidémiologie), Quarantaine (statistiques et données numériques), Santé mondiale (MeSH).
- MESH :
- mortalité : Infections à coronavirus, Pneumopathie virale.
- méthodes : Contrôle des maladies transmissibles.
- pathogénicité : Betacoronavirus.
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- statistiques et données numériques : Quarantaine.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Analyse de regroupements, Humains, Santé mondiale.
English descriptors
- KwdEn :
- Betacoronavirus (pathogenicity), Cluster Analysis (MeSH), Communicable Disease Control (methods), Coronavirus Infections (epidemiology), Coronavirus Infections (mortality), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Global Health (MeSH), Humans (MeSH), Outcome Assessment, Health Care (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (mortality), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission), Quarantine (statistics & numerical data).
- MESH :
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- methods : Communicable Disease Control.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- pathogenicity : Betacoronavirus.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- statistics & numerical data : Quarantine.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Cluster Analysis, Global Health, Humans, Outcome Assessment, Health Care.
Abstract
INTRODUCTION AND AIMS
Retarding the spread of SARS-CoV-2 infection by preventive strategies is the first line of management. Several countries have declared a stringent lockdown in order to enforce social distancing and prevent the spread of infection. This analysis was conducted in an attempt to understand the impact of lockdown on infection and death rates over a period of time in countries with declared lock-down.
MATERIAL AND METHODS
A validated database was used to generate data related to countries with declared lockdown. Simple regression analysis was conducted to assess the rate of change in infection and death rates. Subsequently, a k-means and hierarchical cluster analysis was done to identify the countries that performed similarly. Sweden and South Korea were included as counties without lockdown in a second-phase cluster analysis.
RESULTS
There was a significant 61% and 43% reduction in infection rates 1-week post lockdown in the overall and India cohorts, respectively, supporting its effectiveness. Countries with higher baseline infections and deaths (Spain, Germany, Italy, UK, and France-cluster 1) fared poorly compared to those who declared lockdown early on (Belgium, Austria, New Zealand, India, Hungary, Poland and Malaysia-cluster 2). Sweden and South Korea, countries without lock-down, fared as good as the countries in cluster 2.
CONCLUSION
Lockdown has proven to be an effective strategy is slowing down the SARS-CoV-2 disease progression (infection rate and death) exponentially. The success story of non-lock-down countries (Sweden and South Korea) need to be explored in detail, to identify the variables responsible for the positive results.
DOI: 10.1016/j.dsx.2020.05.026
PubMed: 32426062
PubMed Central: PMC7227592
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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xml:lang="en"><p><b>INTRODUCTION AND AIMS</b></p><p>Retarding the spread of SARS-CoV-2 infection by preventive strategies is the first line of management. Several countries have declared a stringent lockdown in order to enforce social distancing and prevent the spread of infection. This analysis was conducted in an attempt to understand the impact of lockdown on infection and death rates over a period of time in countries with declared lock-down.</p></div><div type="abstract" xml:lang="en"><p><b>MATERIAL AND METHODS</b></p><p>A validated database was used to generate data related to countries with declared lockdown. Simple regression analysis was conducted to assess the rate of change in infection and death rates. Subsequently, a k-means and hierarchical cluster analysis was done to identify the countries that performed similarly. Sweden and South Korea were included as counties without lockdown in a second-phase cluster analysis.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>There was a significant 61% and 43% reduction in infection rates 1-week post lockdown in the overall and India cohorts, respectively, supporting its effectiveness. Countries with higher baseline infections and deaths (Spain, Germany, Italy, UK, and France-cluster 1) fared poorly compared to those who declared lockdown early on (Belgium, Austria, New Zealand, India, Hungary, Poland and Malaysia-cluster 2). Sweden and South Korea, countries without lock-down, fared as good as the countries in cluster 2.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>Lockdown has proven to be an effective strategy is slowing down the SARS-CoV-2 disease progression (infection rate and death) exponentially. The success story of non-lock-down countries (Sweden and South Korea) need to be explored in detail, to identify the variables responsible for the positive results.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32426062</PMID><DateCompleted><Year>2020</Year><Month>07</Month><Day>27</Day></DateCompleted><DateRevised><Year>2020</Year><Month>07</Month><Day>27</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1878-0334</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><Issue>4</Issue><PubDate><MedlineDate>2020 Jul - Aug</MedlineDate></PubDate></JournalIssue><Title>Diabetes & metabolic syndrome</Title><ISOAbbreviation>Diabetes Metab Syndr</ISOAbbreviation></Journal><ArticleTitle>Impact of complete lockdown on total infection and death rates: A hierarchical cluster analysis.</ArticleTitle><Pagination><MedlinePgn>707-711</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S1871-4021(20)30145-4</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.dsx.2020.05.026</ELocationID><Abstract><AbstractText Label="INTRODUCTION AND AIMS" NlmCategory="OBJECTIVE">Retarding the spread of SARS-CoV-2 infection by preventive strategies is the first line of management. Several countries have declared a stringent lockdown in order to enforce social distancing and prevent the spread of infection. This analysis was conducted in an attempt to understand the impact of lockdown on infection and death rates over a period of time in countries with declared lock-down.</AbstractText><AbstractText Label="MATERIAL AND METHODS" NlmCategory="METHODS">A validated database was used to generate data related to countries with declared lockdown. Simple regression analysis was conducted to assess the rate of change in infection and death rates. Subsequently, a k-means and hierarchical cluster analysis was done to identify the countries that performed similarly. Sweden and South Korea were included as counties without lockdown in a second-phase cluster analysis.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">There was a significant 61% and 43% reduction in infection rates 1-week post lockdown in the overall and India cohorts, respectively, supporting its effectiveness. Countries with higher baseline infections and deaths (Spain, Germany, Italy, UK, and France-cluster 1) fared poorly compared to those who declared lockdown early on (Belgium, Austria, New Zealand, India, Hungary, Poland and Malaysia-cluster 2). Sweden and South Korea, countries without lock-down, fared as good as the countries in cluster 2.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">Lockdown has proven to be an effective strategy is slowing down the SARS-CoV-2 disease progression (infection rate and death) exponentially. The success story of non-lock-down countries (Sweden and South Korea) need to be explored in detail, to identify the variables responsible for the positive results.</AbstractText><CopyrightInformation>Copyright © 2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ghosal</LastName><ForeName>Samit</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Nightingale Hospital, Kolkata, India. Electronic address: ramdasghosal@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bhattacharyya</LastName><ForeName>Rahul</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Private practitioner, Kolkata, West Bengal, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Majumder</LastName><ForeName>Milan</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Independent statistician, Kolkata, West Bengal, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>05</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Diabetes Metab Syndr</MedlineTA><NlmUniqueID>101462250</NlmUniqueID><ISSNLinking>1871-4021</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="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName><QualifierName UI="Q000472" MajorTopicYN="Y">pathogenicity</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003140" MajorTopicYN="N">Communicable Disease Control</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014943" MajorTopicYN="N">Global Health</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017063" MajorTopicYN="N">Outcome Assessment, Health Care</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011790" MajorTopicYN="Y">Quarantine</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Cluster analysis</Keyword><Keyword MajorTopicYN="N">Death</Keyword><Keyword MajorTopicYN="N">Infection</Keyword><Keyword MajorTopicYN="N">Lockdown</Keyword><Keyword MajorTopicYN="N">SARS-CoV-2</Keyword></KeywordList><CoiStatement>Declaration of competing interest None to declare.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>08</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>05</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>05</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>5</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>7</Month><Day>28</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>5</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32426062</ArticleId><ArticleId IdType="doi">10.1016/j.dsx.2020.05.026</ArticleId><ArticleId IdType="pii">S1871-4021(20)30145-4</ArticleId><ArticleId IdType="pmc">PMC7227592</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Inde</li></country></list><tree><country name="Inde"><noRegion><name sortKey="Ghosal, Samit" sort="Ghosal, Samit" uniqKey="Ghosal S" first="Samit" last="Ghosal">Samit Ghosal</name></noRegion><name sortKey="Bhattacharyya, Rahul" sort="Bhattacharyya, Rahul" uniqKey="Bhattacharyya R" first="Rahul" last="Bhattacharyya">Rahul Bhattacharyya</name><name sortKey="Majumder, Milan" sort="Majumder, Milan" uniqKey="Majumder M" first="Milan" last="Majumder">Milan Majumder</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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