Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.
Identifieur interne : 000836 ( Main/Corpus ); précédent : 000835; suivant : 000837Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.
Auteurs : Maxwell Salvatore ; Deepankar Basu ; Debashree Ray ; Mike Kleinsasser ; Soumik Purkayastha ; Rupam Bhattacharyya ; Bhramar MukherjeeSource :
- BMJ open [ 2044-6055 ] ; 2020.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : India.
- mortality : COVID-19.
- prevention & control : COVID-19.
- statistics & numerical data : COVID-19 Testing, Quarantine.
- trends : Public Health.
- Humans.
Abstract
OBJECTIVES
To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.
DESIGN
Cohort study (daily time series of case counts).
SETTING
Observational and population based.
PARTICIPANTS
Confirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020.
EXPOSURE
Lockdown (non-medical intervention).
MAIN OUTCOMES AND MEASURES
We illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing.
RESULTS
The estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19-25 March) to 113 372 (25-31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns.
CONCLUSIONS
Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.
DOI: 10.1136/bmjopen-2020-041778
PubMed: 33303462
PubMed Central: PMC7733201
Links to Exploration step
pubmed:33303462Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.</title><author><name sortKey="Salvatore, Maxwell" sort="Salvatore, Maxwell" uniqKey="Salvatore M" first="Maxwell" last="Salvatore">Maxwell Salvatore</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Basu, Deepankar" sort="Basu, Deepankar" uniqKey="Basu D" first="Deepankar" last="Basu">Deepankar Basu</name><affiliation><nlm:affiliation>Department of Economics, University of Massachusetts Amherst, Amherst, Massachusetts, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Ray, Debashree" sort="Ray, Debashree" uniqKey="Ray D" first="Debashree" last="Ray">Debashree Ray</name><affiliation><nlm:affiliation>Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Kleinsasser, Mike" sort="Kleinsasser, Mike" uniqKey="Kleinsasser M" first="Mike" last="Kleinsasser">Mike Kleinsasser</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Purkayastha, Soumik" sort="Purkayastha, Soumik" uniqKey="Purkayastha S" first="Soumik" last="Purkayastha">Soumik Purkayastha</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Bhattacharyya, Rupam" sort="Bhattacharyya, Rupam" uniqKey="Bhattacharyya R" first="Rupam" last="Bhattacharyya">Rupam Bhattacharyya</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Mukherjee, Bhramar" sort="Mukherjee, Bhramar" uniqKey="Mukherjee B" first="Bhramar" last="Mukherjee">Bhramar Mukherjee</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA bhramar@umich.edu.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:33303462</idno><idno type="pmid">33303462</idno><idno type="doi">10.1136/bmjopen-2020-041778</idno><idno type="pmc">PMC7733201</idno><idno type="wicri:Area/Main/Corpus">000836</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000836</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.</title><author><name sortKey="Salvatore, Maxwell" sort="Salvatore, Maxwell" uniqKey="Salvatore M" first="Maxwell" last="Salvatore">Maxwell Salvatore</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Basu, Deepankar" sort="Basu, Deepankar" uniqKey="Basu D" first="Deepankar" last="Basu">Deepankar Basu</name><affiliation><nlm:affiliation>Department of Economics, University of Massachusetts Amherst, Amherst, Massachusetts, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Ray, Debashree" sort="Ray, Debashree" uniqKey="Ray D" first="Debashree" last="Ray">Debashree Ray</name><affiliation><nlm:affiliation>Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Kleinsasser, Mike" sort="Kleinsasser, Mike" uniqKey="Kleinsasser M" first="Mike" last="Kleinsasser">Mike Kleinsasser</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Purkayastha, Soumik" sort="Purkayastha, Soumik" uniqKey="Purkayastha S" first="Soumik" last="Purkayastha">Soumik Purkayastha</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Bhattacharyya, Rupam" sort="Bhattacharyya, Rupam" uniqKey="Bhattacharyya R" first="Rupam" last="Bhattacharyya">Rupam Bhattacharyya</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Mukherjee, Bhramar" sort="Mukherjee, Bhramar" uniqKey="Mukherjee B" first="Bhramar" last="Mukherjee">Bhramar Mukherjee</name><affiliation><nlm:affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA bhramar@umich.edu.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan, USA.</nlm:affiliation></affiliation></author></analytic><series><title level="j">BMJ open</title><idno type="eISSN">2044-6055</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>COVID-19 (mortality)</term><term>COVID-19 (prevention & control)</term><term>COVID-19 Testing (statistics & numerical data)</term><term>Humans (MeSH)</term><term>India (epidemiology)</term><term>Public Health (trends)</term><term>Quarantine (statistics & numerical data)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>India</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>COVID-19</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>COVID-19</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>COVID-19 Testing</term><term>Quarantine</term></keywords><keywords scheme="MESH" qualifier="trends" xml:lang="en"><term>Public Health</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVES</b></p><p>To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.</p></div><div type="abstract" xml:lang="en"><p><b>DESIGN</b></p><p>Cohort study (daily time series of case counts).</p></div><div type="abstract" xml:lang="en"><p><b>SETTING</b></p><p>Observational and population based.</p></div><div type="abstract" xml:lang="en"><p><b>PARTICIPANTS</b></p><p>Confirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020.</p></div><div type="abstract" xml:lang="en"><p><b>EXPOSURE</b></p><p>Lockdown (non-medical intervention).</p></div><div type="abstract" xml:lang="en"><p><b>MAIN OUTCOMES AND MEASURES</b></p><p>We illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19-25 March) to 113 372 (25-31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">33303462</PMID><DateCompleted><Year>2020</Year><Month>12</Month><Day>29</Day></DateCompleted><DateRevised><Year>2021</Year><Month>01</Month><Day>10</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2044-6055</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>12</Issue><PubDate><Year>2020</Year><Month>12</Month><Day>10</Day></PubDate></JournalIssue><Title>BMJ open</Title><ISOAbbreviation>BMJ Open</ISOAbbreviation></Journal><ArticleTitle>Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.</ArticleTitle><Pagination><MedlinePgn>e041778</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1136/bmjopen-2020-041778</ELocationID><Abstract><AbstractText Label="OBJECTIVES">To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.</AbstractText><AbstractText Label="DESIGN">Cohort study (daily time series of case counts).</AbstractText><AbstractText Label="SETTING">Observational and population based.</AbstractText><AbstractText Label="PARTICIPANTS">Confirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020.</AbstractText><AbstractText Label="EXPOSURE">Lockdown (non-medical intervention).</AbstractText><AbstractText Label="MAIN OUTCOMES AND MEASURES">We illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing.</AbstractText><AbstractText Label="RESULTS">The estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19-25 March) to 113 372 (25-31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns.</AbstractText><AbstractText Label="CONCLUSIONS">Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.</AbstractText><CopyrightInformation>© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Salvatore</LastName><ForeName>Maxwell</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Basu</LastName><ForeName>Deepankar</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Economics, University of Massachusetts Amherst, Amherst, Massachusetts, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ray</LastName><ForeName>Debashree</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kleinsasser</LastName><ForeName>Mike</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Purkayastha</LastName><ForeName>Soumik</ForeName><Initials>S</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-3619-2804</Identifier><AffiliationInfo><Affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bhattacharyya</LastName><ForeName>Rupam</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mukherjee</LastName><ForeName>Bhramar</ForeName><Initials>B</Initials><Identifier Source="ORCID">http://orcid.org/0000-0003-0118-4561</Identifier><AffiliationInfo><Affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA bhramar@umich.edu.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, Michigan, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>P30 CA046592</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D064888">Observational Study</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>12</Month><Day>10</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMJ Open</MedlineTA><NlmUniqueID>101552874</NlmUniqueID><ISSNLinking>2044-6055</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="UpdateOf"><RefSource>medRxiv. 2020 Jun 14;:</RefSource><PMID Version="1">32587995</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000086742" MajorTopicYN="N">COVID-19 Testing</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007194" MajorTopicYN="N" Type="Geographic">India</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011634" MajorTopicYN="N">Public Health</DescriptorName><QualifierName UI="Q000639" MajorTopicYN="Y">trends</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011790" MajorTopicYN="N">Quarantine</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">epidemiology</Keyword><Keyword MajorTopicYN="Y">public health</Keyword><Keyword MajorTopicYN="Y">statistics & research methods</Keyword></KeywordList><CoiStatement>Competing interests: None declared.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>12</Month><Day>11</Day><Hour>5</Hour><Minute>50</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>12</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>12</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33303462</ArticleId><ArticleId IdType="pii">bmjopen-2020-041778</ArticleId><ArticleId IdType="doi">10.1136/bmjopen-2020-041778</ArticleId><ArticleId IdType="pmc">PMC7733201</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Lancet. 2020 May 30;395(10238):1687-1688</Citation><ArticleIdList><ArticleId IdType="pubmed">32539939</ArticleId></ArticleIdList></Reference><Reference><Citation>Harv Data Sci Rev. 2020;2020(Suppl 1):</Citation><ArticleIdList><ArticleId IdType="pubmed">32607504</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Epidemiol. 2005 Sep 1;162(5):479-86</Citation><ArticleIdList><ArticleId IdType="pubmed">16076827</ArticleId></ArticleIdList></Reference><Reference><Citation>Nature. 2020 Aug;584(7821):420-424</Citation><ArticleIdList><ArticleId IdType="pubmed">32674112</ArticleId></ArticleIdList></Reference><Reference><Citation>J Epidemiol Glob Health. 2020 Aug 28;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32959618</ArticleId></ArticleIdList></Reference><Reference><Citation>JMIR Public Health Surveill. 2020 Aug 12;6(3):e20341</Citation><ArticleIdList><ArticleId IdType="pubmed">32763888</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2020 Sep 16;15(9):e0239026</Citation><ArticleIdList><ArticleId IdType="pubmed">32936811</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Infect Dis. 2020 Feb;91:264-266</Citation><ArticleIdList><ArticleId IdType="pubmed">31953166</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Epidemiol. 2013 Nov 1;178(9):1505-12</Citation><ArticleIdList><ArticleId IdType="pubmed">24043437</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Infect Dis. 2020 Dec 3;:</Citation><ArticleIdList><ArticleId IdType="pubmed">33279653</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 May 19;323(19):1893-1894</Citation><ArticleIdList><ArticleId IdType="pubmed">32297897</ArticleId></ArticleIdList></Reference><Reference><Citation>Nat Med. 2020 Apr;26(4):506-510</Citation><ArticleIdList><ArticleId IdType="pubmed">32284616</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 May 19;323(19):1915-1923</Citation><ArticleIdList><ArticleId IdType="pubmed">32275295</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/LockdownV1/Data/Main/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000836 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Corpus/biblio.hfd -nk 000836 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= LockdownV1
|flux= Main
|étape= Corpus
|type= RBID
|clé= pubmed:33303462
|texte= Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Corpus/RBID.i -Sk "pubmed:33303462" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Corpus/biblio.hfd \
| NlmPubMed2Wicri -a LockdownV1
| This area was generated with Dilib version V0.6.38. |  |