Prediction of CoVid-19 infection, transmission and recovery rates: A new analysis and global societal comparisons.
Identifieur interne : 000910 ( Main/Exploration ); précédent : 000909; suivant : 000911Prediction of CoVid-19 infection, transmission and recovery rates: A new analysis and global societal comparisons.
Auteurs : Romney B. Duffey [États-Unis] ; Enrico Zio [France, Italie, Corée du Sud]Source :
- Safety science [ 0925-7535 ] ; 2020.
Abstract
We analyze the process of infection rate growth and decline for the recent global pandemic, applying a new method to the available global data. We describe and utilize an original approach based on statistical physics to predict the societal transmission timescale and the universal recovery trajectory resulting from the countermeasures implemented in entire societies. We compare the whole-society infection growth rates for many countries and local regions, to illustrate the common physical and mathematical basis for the viral spread and infection rate reduction, and validate the theory and resulting correlations. We show that methods traditionally considered for the numerical analysis and the control of individual virus transmission (e.g. ℜ0 scaling) represent one special case of the theory, and also compare our results to the available IHME computer model outcomes. We proceed to illustrate several interesting features of the different approaches to the mitigation of the pandemic, related to social isolation and "lockdown" tactics. Finally, we use presently available data from many countries to make actual predictions of the time needed for securing minimum infection rates in the future, highlighting the differences that emerge between isolated "islands" and mobile cities, and identifying the desired overall recovery trajectory.
DOI: 10.1016/j.ssci.2020.104854
PubMed: 32518471
PubMed Central: PMC7254020
Affiliations:
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We describe and utilize an original approach based on statistical physics to predict the societal transmission timescale and the universal recovery trajectory resulting from the countermeasures implemented in entire societies. We compare the whole-society infection growth rates for many countries and local regions, to illustrate the common physical and mathematical basis for the viral spread and infection rate reduction, and validate the theory and resulting correlations. We show that methods traditionally considered for the numerical analysis and the control of individual virus transmission (e.g. ℜ<sub>0</sub> scaling) represent one special case of the theory, and also compare our results to the available IHME computer model outcomes. We proceed to illustrate several interesting features of the different approaches to the mitigation of the pandemic, related to social isolation and "lockdown" tactics. Finally, we use presently available data from many countries to make actual predictions of the time needed for securing minimum infection rates in the future, highlighting the differences that emerge between isolated "islands" and mobile cities, and identifying the desired overall recovery trajectory.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32518471</PMID><DateRevised><Year>2020</Year><Month>06</Month><Day>12</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0925-7535</ISSN><JournalIssue CitedMedium="Print"><Volume>129</Volume><PubDate><Year>2020</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Safety science</Title><ISOAbbreviation>Saf Sci</ISOAbbreviation></Journal><ArticleTitle>Prediction of CoVid-19 infection, transmission and recovery rates: A new analysis and global societal comparisons.</ArticleTitle><Pagination><MedlinePgn>104854</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.ssci.2020.104854</ELocationID><Abstract><AbstractText>We analyze the process of infection rate growth and decline for the recent global pandemic, applying a new method to the available global data. We describe and utilize an original approach based on statistical physics to predict the societal transmission timescale and the universal recovery trajectory resulting from the countermeasures implemented in entire societies. We compare the whole-society infection growth rates for many countries and local regions, to illustrate the common physical and mathematical basis for the viral spread and infection rate reduction, and validate the theory and resulting correlations. We show that methods traditionally considered for the numerical analysis and the control of individual virus transmission (e.g. ℜ<sub>0</sub> scaling) represent one special case of the theory, and also compare our results to the available IHME computer model outcomes. We proceed to illustrate several interesting features of the different approaches to the mitigation of the pandemic, related to social isolation and "lockdown" tactics. Finally, we use presently available data from many countries to make actual predictions of the time needed for securing minimum infection rates in the future, highlighting the differences that emerge between isolated "islands" and mobile cities, and identifying the desired overall recovery trajectory.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Duffey</LastName><ForeName>Romney B</ForeName><Initials>RB</Initials><AffiliationInfo><Affiliation>Idaho Falls, ID, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zio</LastName><ForeName>Enrico</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Centre de Recherche sur les Risques et les Crises (CRC), MINES ParisTech/PSL Université Paris, Sophia Antipolis, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Energy, Politecnico di Milano, Milan, Italy.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Eminent Scholar at the Department of Nuclear Engineering, Kyung Hee University, Seoul, South Korea.</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>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Saf Sci</MedlineTA><NlmUniqueID>9114980</NlmUniqueID><ISSNLinking>0925-7535</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">CoVid-19</Keyword><Keyword MajorTopicYN="N">Infection rate</Keyword><Keyword MajorTopicYN="N">Learning Theory</Keyword><Keyword MajorTopicYN="N">Pandemic risk</Keyword><Keyword MajorTopicYN="N">Universal Recovery Curve</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>05</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>6</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>6</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>6</Month><Day>11</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32518471</ArticleId><ArticleId IdType="doi">10.1016/j.ssci.2020.104854</ArticleId><ArticleId IdType="pii">S0925-7535(20)30251-4</ArticleId><ArticleId IdType="pii">104854</ArticleId><ArticleId IdType="pmc">PMC7254020</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
<ReferenceList><Reference><Citation>Euro Surveill. 2020 Feb;25(5):</Citation><ArticleIdList><ArticleId IdType="pubmed">32046819</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2015 Mar 20;10(3):e0120567</Citation><ArticleIdList><ArticleId IdType="pubmed">25793764</ArticleId></ArticleIdList></Reference><Reference><Citation>Acta Biotheor. 2002;50(3):189-204</Citation><ArticleIdList><ArticleId IdType="pubmed">12211331</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet Infect Dis. 2020 May;20(5):553-558</Citation><ArticleIdList><ArticleId IdType="pubmed">32171059</ArticleId></ArticleIdList></Reference><Reference><Citation>MMWR Morb Mortal Wkly Rep. 2020 Apr 17;69(15):446-450</Citation><ArticleIdList><ArticleId IdType="pubmed">32298246</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Corée du Sud</li><li>France</li><li>Italie</li><li>États-Unis</li></country><region><li>Idaho</li><li>Lombardie</li><li>Région capitale de Séoul</li></region><settlement><li>Milan</li><li>Séoul</li></settlement></list><tree><country name="États-Unis"><region name="Idaho"><name sortKey="Duffey, Romney B" sort="Duffey, Romney B" uniqKey="Duffey R" first="Romney B" last="Duffey">Romney B. Duffey</name></region></country><country name="France"><noRegion><name sortKey="Zio, Enrico" sort="Zio, Enrico" uniqKey="Zio E" first="Enrico" last="Zio">Enrico Zio</name></noRegion></country><country name="Italie"><region name="Lombardie"><name sortKey="Zio, Enrico" sort="Zio, Enrico" uniqKey="Zio E" first="Enrico" last="Zio">Enrico Zio</name></region></country><country name="Corée du Sud"><region name="Région capitale de Séoul"><name sortKey="Zio, Enrico" sort="Zio, Enrico" uniqKey="Zio E" first="Enrico" last="Zio">Enrico Zio</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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