SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions.
Identifieur interne : 000682 ( Main/Exploration ); précédent : 000681; suivant : 000683SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions.
Auteurs : Archisman Mazumder [Inde] ; Mehak Arora [Inde] ; Vishwesh Bharadiya [Inde] ; Parul Berry [Inde] ; Mudit Agarwal [Inde] ; Priyamadhaba Behera [Inde] ; Hemant Deepak Shewade [Inde, France] ; Ayush Lohiya [Inde] ; Mohak Gupta [Inde] ; Aditi Rao [Inde] ; Giridara Gopal Parameswaran [Inde]Source :
- F1000Research [ 2046-1402 ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Betacoronavirus (MeSH), Contrôle des maladies transmissibles (MeSH), Femelle (MeSH), Humains (MeSH), Inde (épidémiologie), Infections à coronavirus (mortalité), Infections à coronavirus (épidémiologie), Jeune adulte (MeSH), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (mortalité), Pneumopathie virale (épidémiologie), Répartition par sexe (MeSH), Répartition par âge (MeSH), Études transversales (MeSH).
- MESH :
- mortalité : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Inde, Infections à coronavirus, Pneumopathie virale.
- Adulte, Adulte d'âge moyen, Betacoronavirus, Contrôle des maladies transmissibles, Femelle, Humains, Jeune adulte, Mâle, Pandémies, Répartition par sexe, Répartition par âge, Études transversales.
- Wicri :
- geographic : Inde.
English descriptors
- KwdEn :
- Adult (MeSH), Age Distribution (MeSH), Betacoronavirus (MeSH), Communicable Disease Control (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (mortality), Cross-Sectional Studies (MeSH), Female (MeSH), Humans (MeSH), India (epidemiology), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Pneumonia, Viral (mortality), Sex Distribution (MeSH), Young Adult (MeSH).
- MESH :
- geographic , epidemiology : India.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- Adult, Age Distribution, Betacoronavirus, Communicable Disease Control, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Pandemics, Sex Distribution, Young Adult.
Abstract
Background: After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age, gender distribution, and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of COVID-19 infections after 40 days of lockdown. Methods: We used a cross-sectional descriptive design for the first objective and a susceptible-infected-removed model for in silico predictions. We collected data from government-controlled and crowdsourced websites. Results: Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. Conclusion: The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India but will be unable to prevent the spike in the number of cases.
DOI: 10.12688/f1000research.23496.2
PubMed: 32528664
PubMed Central: PMC7262570.2
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Sciences, New Delhi, Delhi, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>All India Institute of Medical Sciences, New Delhi, Delhi</wicri:regionArea><wicri:noRegion>Delhi</wicri:noRegion></affiliation></author><author><name sortKey="Arora, Mehak" sort="Arora, Mehak" uniqKey="Arora M" first="Mehak" last="Arora">Mehak Arora</name><affiliation wicri:level="1"><nlm:affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>All India Institute of Medical Sciences, New Delhi, Delhi</wicri:regionArea><wicri:noRegion>Delhi</wicri:noRegion></affiliation></author><author><name sortKey="Bharadiya, Vishwesh" sort="Bharadiya, Vishwesh" uniqKey="Bharadiya V" first="Vishwesh" last="Bharadiya">Vishwesh Bharadiya</name><affiliation wicri:level="1"><nlm:affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</nlm:affiliation><country 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Giridara Gopal" sort="Parameswaran, Giridara Gopal" uniqKey="Parameswaran G" first="Giridara Gopal" last="Parameswaran">Giridara Gopal Parameswaran</name><affiliation wicri:level="1"><nlm:affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>All India Institute of Medical Sciences, New Delhi, Delhi</wicri:regionArea><wicri:noRegion>Delhi</wicri:noRegion></affiliation></author></analytic><series><title level="j">F1000Research</title><idno type="eISSN">2046-1402</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Age Distribution (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Communicable Disease Control (MeSH)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (mortality)</term><term>Cross-Sectional Studies (MeSH)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>India (epidemiology)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (mortality)</term><term>Sex Distribution (MeSH)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Contrôle des maladies transmissibles (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Inde (épidémiologie)</term><term>Infections à coronavirus (mortalité)</term><term>Infections à coronavirus (épidémiologie)</term><term>Jeune adulte (MeSH)</term><term>Mâle (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (mortalité)</term><term>Pneumopathie virale (épidémiologie)</term><term>Répartition par sexe (MeSH)</term><term>Répartition par âge (MeSH)</term><term>Études transversales (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>India</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="mortalité" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Inde</term><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Age Distribution</term><term>Betacoronavirus</term><term>Communicable Disease Control</term><term>Cross-Sectional Studies</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Pandemics</term><term>Sex Distribution</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Betacoronavirus</term><term>Contrôle des maladies transmissibles</term><term>Femelle</term><term>Humains</term><term>Jeune adulte</term><term>Mâle</term><term>Pandémies</term><term>Répartition par sexe</term><term>Répartition par âge</term><term>Études transversales</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Inde</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><b>Background</b>: After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age, gender distribution, and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of COVID-19 infections after 40 days of lockdown. <b>Methods</b>: We used a cross-sectional descriptive design for the first objective and a susceptible-infected-removed model for <i>in silico</i> predictions. We collected data from government-controlled and crowdsourced websites. <b>Results</b>: Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. <b>Conclusion</b>: The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India but will be unable to prevent the spike in the number of cases.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" VersionID="2" VersionDate="2020/06/29" IndexingMethod="Curated" Owner="NLM"><PMID Version="2">32528664</PMID><DateCompleted><Year>2020</Year><Month>07</Month><Day>07</Day></DateCompleted><DateRevised><Year>2020</Year><Month>07</Month><Day>07</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">2046-1402</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>F1000Research</Title><ISOAbbreviation>F1000Res</ISOAbbreviation></Journal><ArticleTitle>SARS-CoV-2 epidemic in India: epidemiological features and <i>in silico</i> analysis of the effect of interventions.</ArticleTitle><Pagination><MedlinePgn>315</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.12688/f1000research.23496.2</ELocationID><Abstract><AbstractText><b>Background</b>: After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age, gender distribution, and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of COVID-19 infections after 40 days of lockdown. <b>Methods</b>: We used a cross-sectional descriptive design for the first objective and a susceptible-infected-removed model for <i>in silico</i> predictions. We collected data from government-controlled and crowdsourced websites. <b>Results</b>: Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. <b>Conclusion</b>: The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India but will be unable to prevent the spike in the number of cases.</AbstractText><CopyrightInformation>Copyright: © 2020 Mazumder A et al.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Mazumder</LastName><ForeName>Archisman</ForeName><Initials>A</Initials><Identifier Source="ORCID">0000-0001-9150-8800</Identifier><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Arora</LastName><ForeName>Mehak</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0003-1225-9549</Identifier><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bharadiya</LastName><ForeName>Vishwesh</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Berry</LastName><ForeName>Parul</ForeName><Initials>P</Initials><Identifier Source="ORCID">0000-0002-0860-4496</Identifier><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Agarwal</LastName><ForeName>Mudit</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Behera</LastName><ForeName>Priyamadhaba</ForeName><Initials>P</Initials><Identifier Source="ORCID">0000-0001-9318-4483</Identifier><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, Raebareli, Uttar Pradesh, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shewade</LastName><ForeName>Hemant Deepak</ForeName><Initials>HD</Initials><Identifier Source="ORCID">0000-0002-8242-1911</Identifier><AffiliationInfo><Affiliation>Operational Research, The Union South-East Asia Office, New Delhi, Delhi, India.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre For Operational Research, International Union Against Tuberculosis and Lung Disease (The Union), Paris, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lohiya</LastName><ForeName>Ayush</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Super Specialty Cancer Institute & Hospital, Lucknow, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gupta</LastName><ForeName>Mohak</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0002-9431-606X</Identifier><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rao</LastName><ForeName>Aditi</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Parameswaran</LastName><ForeName>Giridara Gopal</ForeName><Initials>GG</Initials><AffiliationInfo><Affiliation>All India Institute of Medical Sciences, New Delhi, Delhi, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><DataBankList CompleteYN="Y"><DataBank><DataBankName>figshare</DataBankName><AccessionNumberList><AccessionNumber>10.6084/m9.figshare.c.4940472.v2</AccessionNumber></AccessionNumberList></DataBank></DataBankList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate 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MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017678" MajorTopicYN="N">Sex Distribution</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">COVID-19</Keyword><Keyword MajorTopicYN="Y">Coronavirus</Keyword><Keyword MajorTopicYN="Y">India</Keyword><Keyword MajorTopicYN="Y">SIR model</Keyword></KeywordList><CoiStatement>No competing interests were disclosed.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>06</Month><Day>15</Day></PubMedPubDate><PubMedPubDate 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IdType="pubmed">32201889</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 Apr 16;382(16):1564-1567</Citation><ArticleIdList><ArticleId IdType="pubmed">32182409</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Feb 15;395(10223):507-513</Citation><ArticleIdList><ArticleId IdType="pubmed">32007143</ArticleId></ArticleIdList></Reference><Reference><Citation>Indian J Med Res. 2020 Feb & Mar;151(2 & 3):236-240</Citation><ArticleIdList><ArticleId IdType="pubmed">32362647</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 Mar 26;382(13):1199-1207</Citation><ArticleIdList><ArticleId IdType="pubmed">31995857</ArticleId></ArticleIdList></Reference><Reference><Citation>Clin Microbiol Infect. 2013 Nov;19(11):999-1005</Citation><ArticleIdList><ArticleId IdType="pubmed">24266045</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 Feb 24;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32091533</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 Mar 5;382(10):970-971</Citation><ArticleIdList><ArticleId IdType="pubmed">32003551</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Tuberc Lung Dis. 2020 Jun 1;24(6):647-650</Citation><ArticleIdList><ArticleId IdType="pubmed">32552999</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>France</li><li>Inde</li></country><region><li>Île-de-France</li></region><settlement><li>Paris</li></settlement></list><tree><country name="Inde"><noRegion><name sortKey="Mazumder, Archisman" sort="Mazumder, Archisman" uniqKey="Mazumder A" first="Archisman" last="Mazumder">Archisman Mazumder</name></noRegion><name sortKey="Agarwal, Mudit" sort="Agarwal, Mudit" uniqKey="Agarwal M" first="Mudit" last="Agarwal">Mudit Agarwal</name><name sortKey="Arora, Mehak" sort="Arora, Mehak" uniqKey="Arora M" first="Mehak" last="Arora">Mehak Arora</name><name sortKey="Behera, Priyamadhaba" sort="Behera, Priyamadhaba" uniqKey="Behera P" first="Priyamadhaba" last="Behera">Priyamadhaba Behera</name><name sortKey="Berry, Parul" sort="Berry, Parul" uniqKey="Berry P" first="Parul" last="Berry">Parul Berry</name><name sortKey="Bharadiya, Vishwesh" sort="Bharadiya, Vishwesh" uniqKey="Bharadiya V" first="Vishwesh" last="Bharadiya">Vishwesh Bharadiya</name><name sortKey="Gupta, Mohak" sort="Gupta, Mohak" uniqKey="Gupta M" first="Mohak" last="Gupta">Mohak Gupta</name><name sortKey="Lohiya, Ayush" sort="Lohiya, Ayush" uniqKey="Lohiya A" first="Ayush" last="Lohiya">Ayush Lohiya</name><name sortKey="Parameswaran, Giridara Gopal" sort="Parameswaran, Giridara Gopal" uniqKey="Parameswaran G" first="Giridara Gopal" last="Parameswaran">Giridara Gopal Parameswaran</name><name sortKey="Rao, Aditi" sort="Rao, Aditi" uniqKey="Rao A" first="Aditi" last="Rao">Aditi Rao</name><name sortKey="Shewade, Hemant Deepak" sort="Shewade, Hemant Deepak" uniqKey="Shewade H" first="Hemant Deepak" last="Shewade">Hemant Deepak Shewade</name></country><country name="France"><region name="Île-de-France"><name sortKey="Shewade, Hemant Deepak" sort="Shewade, Hemant Deepak" uniqKey="Shewade H" first="Hemant Deepak" last="Shewade">Hemant Deepak Shewade</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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