"Stay nearby or get checked": A Covid-19 control strategy.
Identifieur interne : 000665 ( Main/Exploration ); précédent : 000664; suivant : 000666"Stay nearby or get checked": A Covid-19 control strategy.
Auteurs : Jan-Tino Brethouwer [Pays-Bas] ; Arnout Van De Rijt [Italie, Pays-Bas] ; Roy Lindelauf [Pays-Bas] ; Robbert Fokkink [Pays-Bas]Source :
- Infectious Disease Modelling [ 2468-0427 ] ; 2021.
Abstract
This paper repurposes the classic insight from network theory that long-distance connections drive disease propagation into a strategy for controlling a second wave of Covid-19. We simulate a scenario in which a lockdown is first imposed on a population and then partly lifted while long-range transmission is kept at a minimum. Simulated spreading patterns resemble contemporary distributions of Covid- 19 across EU member states, German and Italian regions, and through New York City, providing some model validation. Results suggest that our proposed strategy may significantly reduce peak infection. We also find that post-lockdown flare-ups remain local longer, aiding geographical containment. These results suggest a tailored policy in which individuals who frequently travel to places where they interact with many people are offered greater protection, tracked more closely, and are regularly tested. This policy can be communicated to the general public as a simple and reasonable principle: Stay nearby or get checked.
DOI: 10.1016/j.idm.2020.10.013
PubMed: 33225114
PubMed Central: PMC7669247
Affiliations:
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Rijt">Arnout Van De Rijt</name><affiliation wicri:level="1"><nlm:affiliation>European University Institute, Political and Social Sciences, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>European University Institute, Political and Social Sciences</wicri:regionArea><wicri:noRegion>Political and Social Sciences</wicri:noRegion></affiliation><affiliation wicri:level="4"><nlm:affiliation>Utrecht University, Sociology, Netherlands.</nlm:affiliation><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Utrecht University, Sociology</wicri:regionArea><orgName type="university">Université d'Utrecht</orgName><placeName><settlement type="city">Utrecht</settlement><region nuts="2">Utrecht (province)</region></placeName></affiliation></author><author><name sortKey="Lindelauf, Roy" sort="Lindelauf, Roy" uniqKey="Lindelauf R" first="Roy" last="Lindelauf">Roy Lindelauf</name><affiliation wicri:level="1"><nlm:affiliation>Netherlands Defence Academy, Faculty of Military Science, Intelligence and Security, Netherlands.</nlm:affiliation><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Netherlands Defence Academy, Faculty of Military Science, Intelligence and Security</wicri:regionArea><wicri:noRegion>Intelligence and Security</wicri:noRegion></affiliation></author><author><name sortKey="Fokkink, Robbert" sort="Fokkink, Robbert" uniqKey="Fokkink R" first="Robbert" last="Fokkink">Robbert Fokkink</name><affiliation wicri:level="1"><nlm:affiliation>TU Delft, Delft Institute of Applied Mathematics, Netherlands.</nlm:affiliation><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>TU Delft, Delft Institute of Applied Mathematics</wicri:regionArea><wicri:noRegion>Delft Institute of Applied Mathematics</wicri:noRegion></affiliation></author></analytic><series><title level="j">Infectious Disease Modelling</title><idno type="eISSN">2468-0427</idno><imprint><date when="2021" type="published">2021</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper repurposes the classic insight from network theory that long-distance connections drive disease propagation into a strategy for controlling a second wave of Covid-19. We simulate a scenario in which a lockdown is first imposed on a population and then partly lifted while long-range transmission is kept at a minimum. Simulated spreading patterns resemble contemporary distributions of Covid- 19 across EU member states, German and Italian regions, and through New York City, providing some model validation. Results suggest that our proposed strategy may significantly reduce peak infection. We also find that post-lockdown flare-ups remain local longer, aiding geographical containment. These results suggest a tailored policy in which individuals who frequently travel to places where they interact with many people are offered greater protection, tracked more closely, and are regularly tested. This policy can be communicated to the general public as a simple and reasonable principle: Stay nearby or get checked.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">33225114</PMID><DateRevised><Year>2021</Year><Month>01</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2468-0427</ISSN><JournalIssue CitedMedium="Internet"><Volume>6</Volume><PubDate><Year>2021</Year></PubDate></JournalIssue><Title>Infectious Disease Modelling</Title><ISOAbbreviation>Infect Dis Model</ISOAbbreviation></Journal><ArticleTitle>"Stay nearby or get checked": A Covid-19 control strategy.</ArticleTitle><Pagination><MedlinePgn>36-45</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.idm.2020.10.013</ELocationID><Abstract><AbstractText>This paper repurposes the classic insight from network theory that long-distance connections drive disease propagation into a strategy for controlling a second wave of Covid-19. We simulate a scenario in which a lockdown is first imposed on a population and then partly lifted while long-range transmission is kept at a minimum. Simulated spreading patterns resemble contemporary distributions of Covid- 19 across EU member states, German and Italian regions, and through New York City, providing some model validation. Results suggest that our proposed strategy may significantly reduce peak infection. We also find that post-lockdown flare-ups remain local longer, aiding geographical containment. These results suggest a tailored policy in which individuals who frequently travel to places where they interact with many people are offered greater protection, tracked more closely, and are regularly tested. This policy can be communicated to the general public as a simple and reasonable principle: Stay nearby or get checked.</AbstractText><CopyrightInformation>© 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Brethouwer</LastName><ForeName>Jan-Tino</ForeName><Initials>JT</Initials><AffiliationInfo><Affiliation>TU Delft, Delft Institute of Applied Mathematics, Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>van de Rijt</LastName><ForeName>Arnout</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>European University Institute, Political and Social Sciences, Italy.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Utrecht University, Sociology, Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lindelauf</LastName><ForeName>Roy</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Netherlands Defence Academy, Faculty of Military Science, Intelligence and Security, Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fokkink</LastName><ForeName>Robbert</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>TU Delft, Delft Institute of Applied Mathematics, Netherlands.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>11</Month><Day>16</Day></ArticleDate></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Infect Dis Model</MedlineTA><NlmUniqueID>101692406</NlmUniqueID><ISSNLinking>2468-0427</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Epidemiology</Keyword><Keyword MajorTopicYN="N">Interventions</Keyword><Keyword MajorTopicYN="N">Lockdown exit</Keyword><Keyword MajorTopicYN="N">SEIR</Keyword><Keyword MajorTopicYN="N">Small-world network</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>10</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>10</Month><Day>31</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>11</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>11</Month><Day>24</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>11</Month><Day>23</Day><Hour>5</Hour><Minute>42</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33225114</ArticleId><ArticleId IdType="doi">10.1016/j.idm.2020.10.013</ArticleId><ArticleId IdType="pii">S2468-0427(20)30067-1</ArticleId><ArticleId IdType="pmc">PMC7669247</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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