Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks.
Identifieur interne : 001163 ( Main/Exploration ); précédent : 001162; suivant : 001164Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks.
Auteurs : Vitor M. Marquioni [Brésil] ; Marcus A M. De Aguiar [Brésil]Source :
- Chaos, solitons, and fractals [ 0960-0779 ] ; 2020.
Abstract
The COVID-19 pandemic led several countries to resort to social distancing, the only known way to slow down the spread of the virus and keep the health system under control. Here we use an individual based model (IBM) to study how the duration, start date and intensity of quarantine affect the height and position of the peak of the infection curve. We show that stochastic effects, inherent to the model dynamics, lead to variable outcomes for the same set of parameters, making it crucial to compute the probability of each result. To simplify the analysis we divide the outcomes in only two categories, that we call best and worst scenarios. Although long and intense quarantine is the best way to end the epidemic, it is very hard to implement in practice. Here we show that relatively short and intense quarantine periods can also be very effective in flattening the infection curve and even killing the virus, but the likelihood of such outcomes are low. Long quarantines of relatively low intensity, on the other hand, can delay the infection peak and reduce its size considerably with more than 50% probability, being a more effective policy than complete lockdown for short periods.
DOI: 10.1016/j.chaos.2020.109999
PubMed: 32834581
PubMed Central: PMC7305517
Affiliations:
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De Aguiar</name><affiliation wicri:level="4"><nlm:affiliation>Instituto de Física 'Gleb Wataghin', Universidade Estadual de Campinas, Campinas, 13083-859, SP, Brazil.</nlm:affiliation><country xml:lang="fr">Brésil</country><wicri:regionArea>Instituto de Física 'Gleb Wataghin', Universidade Estadual de Campinas, Campinas, 13083-859, SP</wicri:regionArea><placeName><region type="state">État de São Paulo</region><settlement type="city">Campinas</settlement></placeName><orgName type="university">Université d'État de Campinas</orgName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32834581</idno><idno type="pmid">32834581</idno><idno type="doi">10.1016/j.chaos.2020.109999</idno><idno type="pmc">PMC7305517</idno><idno type="wicri:Area/Main/Corpus">001B81</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">001B81</idno><idno type="wicri:Area/Main/Curation">001B81</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">001B81</idno><idno type="wicri:Area/Main/Exploration">001B81</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks.</title><author><name sortKey="Marquioni, Vitor M" sort="Marquioni, Vitor M" uniqKey="Marquioni V" first="Vitor M" last="Marquioni">Vitor M. Marquioni</name><affiliation wicri:level="4"><nlm:affiliation>Instituto de Física 'Gleb Wataghin', Universidade Estadual de Campinas, Campinas, 13083-859, SP, Brazil.</nlm:affiliation><country xml:lang="fr">Brésil</country><wicri:regionArea>Instituto de Física 'Gleb Wataghin', Universidade Estadual de Campinas, Campinas, 13083-859, SP</wicri:regionArea><placeName><region type="state">État de São Paulo</region><settlement type="city">Campinas</settlement></placeName><orgName type="university">Université d'État de Campinas</orgName></affiliation></author><author><name sortKey="De Aguiar, Marcus A M" sort="De Aguiar, Marcus A M" uniqKey="De Aguiar M" first="Marcus A M" last="De Aguiar">Marcus A M. De Aguiar</name><affiliation wicri:level="4"><nlm:affiliation>Instituto de Física 'Gleb Wataghin', Universidade Estadual de Campinas, Campinas, 13083-859, SP, Brazil.</nlm:affiliation><country xml:lang="fr">Brésil</country><wicri:regionArea>Instituto de Física 'Gleb Wataghin', Universidade Estadual de Campinas, Campinas, 13083-859, SP</wicri:regionArea><placeName><region type="state">État de São Paulo</region><settlement type="city">Campinas</settlement></placeName><orgName type="university">Université d'État de Campinas</orgName></affiliation></author></analytic><series><title level="j">Chaos, solitons, and fractals</title><idno type="ISSN">0960-0779</idno><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">The COVID-19 pandemic led several countries to resort to social distancing, the only known way to slow down the spread of the virus and keep the health system under control. Here we use an individual based model (IBM) to study how the duration, start date and intensity of quarantine affect the height and position of the peak of the infection curve. We show that stochastic effects, inherent to the model dynamics, lead to variable outcomes for the same set of parameters, making it crucial to compute the probability of each result. To simplify the analysis we divide the outcomes in only two categories, that we call <i>best</i> and <i>worst</i> scenarios. Although long and intense quarantine is the best way to end the epidemic, it is very hard to implement in practice. Here we show that relatively short and intense quarantine periods can also be very effective in flattening the infection curve and even killing the virus, but the likelihood of such outcomes are low. Long quarantines of relatively low intensity, on the other hand, can delay the infection peak and reduce its size considerably with more than 50% probability, being a more effective policy than complete lockdown for short periods.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32834581</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0960-0779</ISSN><JournalIssue CitedMedium="Print"><Volume>138</Volume><PubDate><Year>2020</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Chaos, solitons, and fractals</Title><ISOAbbreviation>Chaos Solitons Fractals</ISOAbbreviation></Journal><ArticleTitle>Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks.</ArticleTitle><Pagination><MedlinePgn>109999</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.chaos.2020.109999</ELocationID><Abstract><AbstractText>The COVID-19 pandemic led several countries to resort to social distancing, the only known way to slow down the spread of the virus and keep the health system under control. Here we use an individual based model (IBM) to study how the duration, start date and intensity of quarantine affect the height and position of the peak of the infection curve. We show that stochastic effects, inherent to the model dynamics, lead to variable outcomes for the same set of parameters, making it crucial to compute the probability of each result. To simplify the analysis we divide the outcomes in only two categories, that we call <i>best</i> and <i>worst</i> scenarios. Although long and intense quarantine is the best way to end the epidemic, it is very hard to implement in practice. Here we show that relatively short and intense quarantine periods can also be very effective in flattening the infection curve and even killing the virus, but the likelihood of such outcomes are low. Long quarantines of relatively low intensity, on the other hand, can delay the infection peak and reduce its size considerably with more than 50% probability, being a more effective policy than complete lockdown for short periods.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Marquioni</LastName><ForeName>Vitor M</ForeName><Initials>VM</Initials><AffiliationInfo><Affiliation>Instituto de Física 'Gleb Wataghin', Universidade Estadual de Campinas, Campinas, 13083-859, SP, Brazil.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>de Aguiar</LastName><ForeName>Marcus A M</ForeName><Initials>MAM</Initials><AffiliationInfo><Affiliation>Instituto de Física 'Gleb Wataghin', Universidade Estadual de Campinas, Campinas, 13083-859, SP, Brazil.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>06</Month><Day>20</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">Complex networks</Keyword><Keyword MajorTopicYN="N">Epidemic outbreak</Keyword><Keyword MajorTopicYN="N">Quarantine</Keyword><Keyword MajorTopicYN="N">SEIR 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>06</Month><Day>04</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>06</Month><Day>08</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">32834581</ArticleId><ArticleId IdType="doi">10.1016/j.chaos.2020.109999</ArticleId><ArticleId IdType="pii">S0960-0779(20)30397-0</ArticleId><ArticleId IdType="pii">109999</ArticleId><ArticleId IdType="pmc">PMC7305517</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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