Spatially-explicit models for exploring COVID-19 lockdown strategies.
Identifieur interne : 001A67 ( Main/Corpus ); précédent : 001A66; suivant : 001A68Spatially-explicit models for exploring COVID-19 lockdown strategies.
Auteurs : David O'Sullivan ; Mark Gahegan ; Dan Exeter ; Benjamin AdamsSource :
- Transactions in GIS : TG [ 1361-1682 ] ; 2020.
Abstract
This article describes two spatially-explicit models created to allow experimentation with different societal responses to the COVID19 pandemic. We outline the work to date on modelling spatially-explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalised models for mitigation provide significant advantages over a 'one size fits all' approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.
DOI: 10.1111/tgis.12660
PubMed: 32837240
PubMed Central: PMC7283721
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We outline the work to date on modelling spatially-explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalised models for mitigation provide significant advantages over a 'one size fits all' approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32837240</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>28</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">1361-1682</ISSN><JournalIssue CitedMedium="Print"><PubDate><Year>2020</Year><Month>May</Month><Day>26</Day></PubDate></JournalIssue><Title>Transactions in GIS : TG</Title><ISOAbbreviation>Trans GIS</ISOAbbreviation></Journal><ArticleTitle>Spatially-explicit models for exploring COVID-19 lockdown strategies.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1111/tgis.12660</ELocationID><Abstract><AbstractText>This article describes two spatially-explicit models created to allow experimentation with different societal responses to the COVID19 pandemic. We outline the work to date on modelling spatially-explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalised models for mitigation provide significant advantages over a 'one size fits all' approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.</AbstractText><CopyrightInformation>This article is protected by copyright. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>O'Sullivan</LastName><ForeName>David</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Geography University of California Berkeley USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gahegan</LastName><ForeName>Mark</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Centre for eResearch Computer Science University of Auckland - City Campus Auckland New Zealand.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Exeter</LastName><ForeName>Dan</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Population Health University of Auckland - City Campus Auckland New Zealand.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Adams</LastName><ForeName>Benjamin</ForeName><Initials>B</Initials><Identifier Source="ORCID">https://orcid.org/0000-0002-1657-9809</Identifier><AffiliationInfo><Affiliation>Geography University of Canterbury Christchurch Canterbury New Zealand.</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>26</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Trans GIS</MedlineTA><NlmUniqueID>101616169</NlmUniqueID><ISSNLinking>1361-1682</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID‐19</Keyword><Keyword MajorTopicYN="N">regional models</Keyword><Keyword MajorTopicYN="N">simulation</Keyword><Keyword MajorTopicYN="N">spatially‐explicit infectious disease models</Keyword></KeywordList></MedlineCitation><PubmedData><History><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>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32837240</ArticleId><ArticleId IdType="doi">10.1111/tgis.12660</ArticleId><ArticleId IdType="pii">TGIS12660</ArticleId><ArticleId IdType="pmc">PMC7283721</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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