Spreading of infections on random graphs: A percolation-type model for COVID-19.
Identifieur interne : 000582 ( Main/Exploration ); précédent : 000581; suivant : 000583Spreading of infections on random graphs: A percolation-type model for COVID-19.
Auteurs : Fabrizio Croccolo [France] ; H Eduardo Roman [Italie]Source :
- Chaos, solitons, and fractals [ 0960-0779 ] ; 2020.
Abstract
We introduce an epidemic spreading model on a network using concepts from percolation theory. The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behaviour of the lattice model, implemented using the same lockdown scheme as for the SIR scheme, are discussed in detail and illustrated with extensive simulations. A comparison between both models is presented for the case of COVID-19 data from the USA. Both fits to the empirical data are very good, but some differences emerge between the two approaches which indicate the usefulness of having an alternative approach to the widespread SIR model.
DOI: 10.1016/j.chaos.2020.110077
PubMed: 32834619
PubMed Central: PMC7332959
Affiliations:
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The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behaviour of the lattice model, implemented using the same lockdown scheme as for the SIR scheme, are discussed in detail and illustrated with extensive simulations. A comparison between both models is presented for the case of COVID-19 data from the USA. Both fits to the empirical data are very good, but some differences emerge between the two approaches which indicate the usefulness of having an alternative approach to the widespread SIR model.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32834619</PMID><DateRevised><Year>2020</Year><Month>08</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0960-0779</ISSN><JournalIssue CitedMedium="Print"><Volume>139</Volume><PubDate><Year>2020</Year><Month>Oct</Month></PubDate></JournalIssue><Title>Chaos, solitons, and fractals</Title><ISOAbbreviation>Chaos Solitons Fractals</ISOAbbreviation></Journal><ArticleTitle>Spreading of infections on random graphs: A percolation-type model for COVID-19.</ArticleTitle><Pagination><MedlinePgn>110077</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.chaos.2020.110077</ELocationID><Abstract><AbstractText>We introduce an epidemic spreading model on a network using concepts from percolation theory. The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behaviour of the lattice model, implemented using the same lockdown scheme as for the SIR scheme, are discussed in detail and illustrated with extensive simulations. A comparison between both models is presented for the case of COVID-19 data from the USA. Both fits to the empirical data are very good, but some differences emerge between the two approaches which indicate the usefulness of having an alternative approach to the widespread SIR model.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Croccolo</LastName><ForeName>Fabrizio</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Universite de Pau et des Pays de l'Adour, E2S UPPA, CNRS, TOTAL, LFCR UMR5150, Anglet, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Roman</LastName><ForeName>H Eduardo</ForeName><Initials>HE</Initials><AffiliationInfo><Affiliation>Department of Physics, University of Milano-Bicocca, Piazza delle Scienze 3, Milan 20126, Italy.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>07</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Chaos Solitons Fractals</MedlineTA><NlmUniqueID>100971564</NlmUniqueID><ISSNLinking>0960-0779</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Critical percolation</Keyword><Keyword MajorTopicYN="N">Monte Carlo simulations</Keyword><Keyword MajorTopicYN="N">Random graphs</Keyword><Keyword MajorTopicYN="N">SIR Model</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>05</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>06</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32834619</ArticleId><ArticleId IdType="doi">10.1016/j.chaos.2020.110077</ArticleId><ArticleId IdType="pii">S0960-0779(20)30474-4</ArticleId><ArticleId IdType="pii">110077</ArticleId><ArticleId IdType="pmc">PMC7332959</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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