A 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 : 002238 ( Main/Corpus ); précédent : 002237; suivant : 002239A Comprehensive Public Health Evaluation of Lockdown as a Non-pharmaceutical Intervention on COVID-19 Spread in India: National Trends Masking State Level Variations.
Auteurs : Deepankar Basu ; Maxwell Salvatore ; Debashree Ray ; Mike Kleinsasser ; Soumik Purkayastha ; Rupam Bhattacharyya ; Bhramar MukherjeeSource :
- medRxiv : the preprint server for health sciences ; 2020.
Abstract
Introduction India has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation. Methods Using data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, 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% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns. Conclusions Patterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays 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 visualization tools that are daily updated at covind19.org.
DOI: 10.1101/2020.05.25.20113043
PubMed: 32587995
PubMed Central: PMC7310653
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Debashree" sort="Ray, Debashree" uniqKey="Ray D" first="Debashree" last="Ray">Debashree Ray</name><affiliation><nlm:affiliation>Department of Epidemiology, Johns Hopkins University, Baltimore, MD 21205, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, 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, MI 48109, 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, MI 48109, 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, MI 48109, 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, MI 48109, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, USA.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32587995</idno><idno type="pmid">32587995</idno><idno 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MI 48109, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, 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, MD 21205, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, 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, MI 48109, 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, MI 48109, 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, MI 48109, 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, MI 48109, USA.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, USA.</nlm:affiliation></affiliation></author></analytic><series><title level="j">medRxiv : the preprint server for health sciences</title><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Introduction India has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation. Methods Using data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, 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% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns. Conclusions Patterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays 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 visualization tools that are daily updated at covind19.org.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32587995</PMID><DateRevised><Year>2021</Year><Month>01</Month><Day>10</Day></DateRevised><Article PubModel="Electronic"><Journal><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Jun</Month><Day>14</Day></PubDate></JournalIssue><Title>medRxiv : the preprint server for health sciences</Title><ISOAbbreviation>medRxiv</ISOAbbreviation></Journal><ArticleTitle>A Comprehensive Public Health Evaluation of Lockdown as a Non-pharmaceutical Intervention on COVID-19 Spread in India: National Trends Masking State Level Variations.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">2020.05.25.20113043</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1101/2020.05.25.20113043</ELocationID><Abstract><AbstractText>Introduction India has been under four phases of a national lockdown from March 25 to May 31 in response to the COVID-19 pandemic. Unmasking the state-wise variation in the effect of the nationwide lockdown on the progression of the pandemic could inform dynamic policy interventions towards containment and mitigation. Methods Using data on confirmed COVID-19 cases across 20 states that accounted for more than 99% of the cumulative case counts in India till May 31, 2020, 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% confidence interval (CI): [3.03, 3.71]) on March 24, whereas the average of estimates from May 25 - May 31 stands at 1.27 (95% CI: [1.26, 1.28]). Similarly, the estimated doubling time across India was at 3.56 days on March 24, and the past 7-day average for the same on May 31 is 14.37 days. The average daily number of tests have increased from 1,717 (March 19-25) to 131,772 (May 25-31) with an estimated testing shortfall of 4.58 million tests nationally by May 31. However, various states exhibit substantial departures from these national patterns. Conclusions Patterns of change over lockdown periods indicate the lockdown has been effective in slowing the spread of the virus nationally. The COVID-19 outbreak in India displays 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 visualization tools that are daily updated at covind19.org.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Basu</LastName><ForeName>Deepankar</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Economics, University of Massachusetts, Amherst, MA 01002, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Salvatore</LastName><ForeName>Maxwell</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, 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, MD 21205, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, 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, MI 48109, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Purkayastha</LastName><ForeName>Soumik</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, 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, MI 48109, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mukherjee</LastName><ForeName>Bhramar</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, 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="D000076942">Preprint</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>06</Month><Day>14</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>medRxiv</MedlineTA><NlmUniqueID>101767986</NlmUniqueID></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="UpdateIn"><RefSource>BMJ Open. 2020 Dec 10;10(12):e041778</RefSource><PMID Version="1">33303462</PMID></CommentsCorrections></CommentsCorrectionsList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>6</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>6</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>6</Month><Day>27</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32587995</ArticleId><ArticleId IdType="doi">10.1101/2020.05.25.20113043</ArticleId><ArticleId IdType="pmc">PMC7310653</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Lancet. 2020 May 30;395(10238):1687-1688</Citation><ArticleIdList><ArticleId IdType="pubmed">32539939</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 May 19;323(19):1893-1894</Citation><ArticleIdList><ArticleId IdType="pubmed">32297897</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>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></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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