Accessing Covid19 epidemic outbreak in Tamilnadu and the impact of lockdown through epidemiological models and dynamic systems.
Identifieur interne : 001620 ( Main/Corpus ); précédent : 001619; suivant : 001621Accessing Covid19 epidemic outbreak in Tamilnadu and the impact of lockdown through epidemiological models and dynamic systems.
Auteurs : Sukumar Rajendran ; Prabhu JayagopalSource :
- Measurement : journal of the International Measurement Confederation [ 0263-2241 ] ; 2021.
Abstract
Despite having a small footprint origin, COVID-19 has expanded its clutches to being a global pandemic with severe consequences threatening the survival of the human species. Despite international communities closing their corridors to reduce the exponential spread of the coronavirus. The need to study the patterns of transmission and spread gains utmost importance at the grass-root level of the social structure. To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the "effective reproductive number" (R0) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. Comparing the different epidemiological models (SIR, SIER) in forecasting and assessing the current and future spread of COVID-19. R0 value has a high spike in densely populated districts with the probable flattening of the curve due to lockdown and the rapid rise after the relaxation of lockdown. As of June 03, 2020, there were 25,872 confirmed cases and 208 deaths in Tamilnadu after two and a half months of lockdown with minimal exceptions. As on June 03, 2020, the information published online by the Tamilnadu state government the fatality is at 1.8% (208/11345 = 1.8%) spread with those aged (0-12) at 1437 and 13-60 at 21,899 and 60+ at 2536 the risk of symptomatic infection increases with age and comorbid conditions.
DOI: 10.1016/j.measurement.2020.108432
PubMed: 32958973
PubMed Central: PMC7494487
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Despite international communities closing their corridors to reduce the exponential spread of the coronavirus. The need to study the patterns of transmission and spread gains utmost importance at the grass-root level of the social structure. To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the "effective reproductive number" (R<sub>0</sub>) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. Comparing the different epidemiological models (SIR, SIER) in forecasting and assessing the current and future spread of COVID-19. R<sub>0</sub> value has a high spike in densely populated districts with the probable flattening of the curve due to lockdown and the rapid rise after the relaxation of lockdown. As of June 03, 2020, there were 25,872 confirmed cases and 208 deaths in Tamilnadu after two and a half months of lockdown with minimal exceptions. As on June 03, 2020, the information published online by the Tamilnadu state government the fatality is at 1.8% (208/11345 = 1.8%) spread with those aged (0-12) at 1437 and 13-60 at 21,899 and 60+ at 2536 the risk of symptomatic infection increases with age and comorbid conditions.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32958973</PMID><DateRevised><Year>2021</Year><Month>01</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0263-2241</ISSN><JournalIssue CitedMedium="Print"><Volume>169</Volume><PubDate><Year>2021</Year><Month>Feb</Month></PubDate></JournalIssue><Title>Measurement : journal of the International Measurement Confederation</Title><ISOAbbreviation>Measurement (Lond)</ISOAbbreviation></Journal><ArticleTitle>Accessing Covid19 epidemic outbreak in Tamilnadu and the impact of lockdown through epidemiological models and dynamic systems.</ArticleTitle><Pagination><MedlinePgn>108432</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.measurement.2020.108432</ELocationID><Abstract><AbstractText>Despite having a small footprint origin, COVID-19 has expanded its clutches to being a global pandemic with severe consequences threatening the survival of the human species. Despite international communities closing their corridors to reduce the exponential spread of the coronavirus. The need to study the patterns of transmission and spread gains utmost importance at the grass-root level of the social structure. To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the "effective reproductive number" (R<sub>0</sub>) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. Comparing the different epidemiological models (SIR, SIER) in forecasting and assessing the current and future spread of COVID-19. R<sub>0</sub> value has a high spike in densely populated districts with the probable flattening of the curve due to lockdown and the rapid rise after the relaxation of lockdown. As of June 03, 2020, there were 25,872 confirmed cases and 208 deaths in Tamilnadu after two and a half months of lockdown with minimal exceptions. As on June 03, 2020, the information published online by the Tamilnadu state government the fatality is at 1.8% (208/11345 = 1.8%) spread with those aged (0-12) at 1437 and 13-60 at 21,899 and 60+ at 2536 the risk of symptomatic infection increases with age and comorbid conditions.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rajendran</LastName><ForeName>Sukumar</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Research Scholar, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jayagopal</LastName><ForeName>Prabhu</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Associate Professor, Department of Software & Systems Engineering, School of Information Technology and Engineering Vellore Institute of Technology, Vellore, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Measurement (Lond)</MedlineTA><NlmUniqueID>101668765</NlmUniqueID><ISSNLinking>0263-2241</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Covid-19</Keyword><Keyword MajorTopicYN="N">Machine learning</Keyword><Keyword MajorTopicYN="N">SIR model</Keyword><Keyword MajorTopicYN="N">Tamilnadu</Keyword></KeywordList><CoiStatement>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>08</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>09</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>9</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>9</Month><Day>23</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>9</Month><Day>22</Day><Hour>5</Hour><Minute>42</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32958973</ArticleId><ArticleId IdType="doi">10.1016/j.measurement.2020.108432</ArticleId><ArticleId IdType="pii">S0263-2241(20)30966-0</ArticleId><ArticleId IdType="pmc">PMC7494487</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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