Individual-based simulation model for COVID-19 transmission in Daegu, Korea.
Identifieur interne : 000135 ( Main/Exploration ); précédent : 000134; suivant : 000136Individual-based simulation model for COVID-19 transmission in Daegu, Korea.
Auteurs : Woo-Sik Son [Corée du Sud]Source :
- Epidemiology and health [ 2092-7193 ] ; 2020.
Descripteurs français
- KwdFr :
- Adolescent (MeSH), Adulte (MeSH), Adulte d'âge moyen (MeSH), Humains (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Jeune adulte (MeSH), Modèles théoriques (MeSH), Pandémies (prévention et contrôle), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (épidémiologie), Prévision (MeSH), République de Corée (épidémiologie), Simulation numérique (MeSH), Villes (épidémiologie), Épidémies (MeSH), Établissements scolaires (organisation et administration).
- MESH :
- organisation et administration : Établissements scolaires.
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale, République de Corée, Villes.
- Adolescent, Adulte, Adulte d'âge moyen, Humains, Jeune adulte, Modèles théoriques, Prévision, Simulation numérique, Épidémies.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), COVID-19 (MeSH), Cities (epidemiology), Computer Simulation (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Epidemics (MeSH), Forecasting (MeSH), Humans (MeSH), Middle Aged (MeSH), Models, Theoretical (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission), Republic of Korea (epidemiology), Schools (organization & administration), Young Adult (MeSH).
- MESH :
- geographic , epidemiology : Cities, Republic of Korea.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- organization & administration : Schools.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Adolescent, Adult, COVID-19, Computer Simulation, Epidemics, Forecasting, Humans, Middle Aged, Models, Theoretical, Young Adult.
Abstract
OBJECTIVES
The aims of this study were to obtain insights into the current coronavirus disease 2019 (COVID-19) epidemic in the city of Daegu, which accounted for 6,482 of the 9,241 confirmed cases in Korea as of March 26, 2020, to predict the future spread, and to analyze the impact of school opening.
METHODS
Using an individual-based model, we simulated the spread of COVID-19 in Daegu. An individual can be infected through close contact with infected people in a household, at work/school, and at religious and social gatherings. We created a synthetic population from census sample data. Then, 9,000 people were randomly selected from the entire population of Daegu and set as members of the Shincheonji Church. We did not take into account population movements to and from other regions in Korea.
RESULTS
Using the individual-based model, the cumulative confirmed cases in Daegu through March 26, 2020, were reproduced, and it was confirmed that the hotspot, i.e., the Shincheonji Church had a different probability of infection than non-hotspot, i.e., the Daegu community. For 3 scenarios (I: school closing, II: school opening after April 6, III: school opening after April 6 and the mean period from symptom onset to hospitalization increasing to 4.3 days), we predicted future changes in the pattern of COVID-19 spread in Daegu.
CONCLUSIONS
Compared to scenario I, it was found that in scenario III, the cumulative number of patients would increase by 107 and the date of occurrence of the last patient would be delayed by 92 days.
DOI: 10.4178/epih.e2020042
PubMed: 32580535
PubMed Central: PMC7644934
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Individual-based simulation model for COVID-19 transmission in Daegu, Korea.</title><author><name sortKey="Son, Woo Sik" sort="Son, Woo Sik" uniqKey="Son W" first="Woo-Sik" last="Son">Woo-Sik Son</name><affiliation wicri:level="1"><nlm:affiliation>National Institute for Mathematical Sciences, Daejeon, Korea.</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>National Institute for Mathematical Sciences, Daejeon</wicri:regionArea><wicri:noRegion>Daejeon</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32580535</idno><idno type="pmid">32580535</idno><idno type="doi">10.4178/epih.e2020042</idno><idno type="pmc">PMC7644934</idno><idno type="wicri:Area/Main/Corpus">000186</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000186</idno><idno type="wicri:Area/Main/Curation">000186</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000186</idno><idno type="wicri:Area/Main/Exploration">000186</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Individual-based simulation model for COVID-19 transmission in Daegu, Korea.</title><author><name sortKey="Son, Woo Sik" sort="Son, Woo Sik" uniqKey="Son W" first="Woo-Sik" last="Son">Woo-Sik Son</name><affiliation wicri:level="1"><nlm:affiliation>National Institute for Mathematical Sciences, Daejeon, Korea.</nlm:affiliation><country xml:lang="fr">Corée du Sud</country><wicri:regionArea>National Institute for Mathematical Sciences, Daejeon</wicri:regionArea><wicri:noRegion>Daejeon</wicri:noRegion></affiliation></author></analytic><series><title level="j">Epidemiology and health</title><idno type="eISSN">2092-7193</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent (MeSH)</term><term>Adult (MeSH)</term><term>COVID-19 (MeSH)</term><term>Cities (epidemiology)</term><term>Computer Simulation (MeSH)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (prevention & control)</term><term>Coronavirus Infections (transmission)</term><term>Epidemics (MeSH)</term><term>Forecasting (MeSH)</term><term>Humans (MeSH)</term><term>Middle Aged (MeSH)</term><term>Models, Theoretical (MeSH)</term><term>Pandemics (prevention & control)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (prevention & control)</term><term>Pneumonia, Viral (transmission)</term><term>Republic of Korea (epidemiology)</term><term>Schools (organization & administration)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent (MeSH)</term><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (prévention et contrôle)</term><term>Infections à coronavirus (transmission)</term><term>Infections à coronavirus (épidémiologie)</term><term>Jeune adulte (MeSH)</term><term>Modèles théoriques (MeSH)</term><term>Pandémies (prévention et contrôle)</term><term>Pneumopathie virale (prévention et contrôle)</term><term>Pneumopathie virale (transmission)</term><term>Pneumopathie virale (épidémiologie)</term><term>Prévision (MeSH)</term><term>République de Corée (épidémiologie)</term><term>Simulation numérique (MeSH)</term><term>Villes (épidémiologie)</term><term>Épidémies (MeSH)</term><term>Établissements scolaires (organisation et administration)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Cities</term><term>Republic of Korea</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="organisation et administration" xml:lang="fr"><term>Établissements scolaires</term></keywords><keywords scheme="MESH" qualifier="organization & administration" xml:lang="en"><term>Schools</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Coronavirus Infections</term><term>Pandemics</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Infections à coronavirus</term><term>Pandémies</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term><term>République de Corée</term><term>Villes</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>COVID-19</term><term>Computer Simulation</term><term>Epidemics</term><term>Forecasting</term><term>Humans</term><term>Middle Aged</term><term>Models, Theoretical</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Humains</term><term>Jeune adulte</term><term>Modèles théoriques</term><term>Prévision</term><term>Simulation numérique</term><term>Épidémies</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVES</b></p><p>The aims of this study were to obtain insights into the current coronavirus disease 2019 (COVID-19) epidemic in the city of Daegu, which accounted for 6,482 of the 9,241 confirmed cases in Korea as of March 26, 2020, to predict the future spread, and to analyze the impact of school opening.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>Using an individual-based model, we simulated the spread of COVID-19 in Daegu. An individual can be infected through close contact with infected people in a household, at work/school, and at religious and social gatherings. We created a synthetic population from census sample data. Then, 9,000 people were randomly selected from the entire population of Daegu and set as members of the Shincheonji Church. We did not take into account population movements to and from other regions in Korea.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Using the individual-based model, the cumulative confirmed cases in Daegu through March 26, 2020, were reproduced, and it was confirmed that the hotspot, i.e., the Shincheonji Church had a different probability of infection than non-hotspot, i.e., the Daegu community. For 3 scenarios (I: school closing, II: school opening after April 6, III: school opening after April 6 and the mean period from symptom onset to hospitalization increasing to 4.3 days), we predicted future changes in the pattern of COVID-19 spread in Daegu.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>Compared to scenario I, it was found that in scenario III, the cumulative number of patients would increase by 107 and the date of occurrence of the last patient would be delayed by 92 days.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32580535</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>12</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2092-7193</ISSN><JournalIssue CitedMedium="Internet"><Volume>42</Volume><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>Epidemiology and health</Title><ISOAbbreviation>Epidemiol Health</ISOAbbreviation></Journal><ArticleTitle>Individual-based simulation model for COVID-19 transmission in Daegu, Korea.</ArticleTitle><Pagination><MedlinePgn>e2020042</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.4178/epih.e2020042</ELocationID><Abstract><AbstractText Label="OBJECTIVES" NlmCategory="OBJECTIVE">The aims of this study were to obtain insights into the current coronavirus disease 2019 (COVID-19) epidemic in the city of Daegu, which accounted for 6,482 of the 9,241 confirmed cases in Korea as of March 26, 2020, to predict the future spread, and to analyze the impact of school opening.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Using an individual-based model, we simulated the spread of COVID-19 in Daegu. An individual can be infected through close contact with infected people in a household, at work/school, and at religious and social gatherings. We created a synthetic population from census sample data. Then, 9,000 people were randomly selected from the entire population of Daegu and set as members of the Shincheonji Church. We did not take into account population movements to and from other regions in Korea.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Using the individual-based model, the cumulative confirmed cases in Daegu through March 26, 2020, were reproduced, and it was confirmed that the hotspot, i.e., the Shincheonji Church had a different probability of infection than non-hotspot, i.e., the Daegu community. For 3 scenarios (I: school closing, II: school opening after April 6, III: school opening after April 6 and the mean period from symptom onset to hospitalization increasing to 4.3 days), we predicted future changes in the pattern of COVID-19 spread in Daegu.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Compared to scenario I, it was found that in scenario III, the cumulative number of patients would increase by 107 and the date of occurrence of the last patient would be delayed by 92 days.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Son</LastName><ForeName>Woo-Sik</ForeName><Initials>WS</Initials><AffiliationInfo><Affiliation>National Institute for Mathematical Sciences, Daejeon, Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><CollectiveName>RISEWIDs Team</CollectiveName></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>06</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>Korea (South)</Country><MedlineTA>Epidemiol Health</MedlineTA><NlmUniqueID>101519472</NlmUniqueID><ISSNLinking>2092-7193</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002947" MajorTopicYN="N" Type="Geographic">Cities</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058872" MajorTopicYN="Y">Epidemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005544" MajorTopicYN="N">Forecasting</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="N">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D056910" MajorTopicYN="N" Type="Geographic">Republic of Korea</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012574" MajorTopicYN="N">Schools</DescriptorName><QualifierName UI="Q000458" MajorTopicYN="N">organization & administration</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Infections</Keyword><Keyword MajorTopicYN="N">Korea</Keyword><Keyword MajorTopicYN="N">Mathematical model</Keyword><Keyword MajorTopicYN="N">Model prediction</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>04</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>06</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>6</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>6</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32580535</ArticleId><ArticleId IdType="pii">epih.e2020042</ArticleId><ArticleId IdType="doi">10.4178/epih.e2020042</ArticleId><ArticleId IdType="pmc">PMC7644934</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Nature. 2005 Sep 8;437(7056):209-14</Citation><ArticleIdList><ArticleId IdType="pubmed">16079797</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2012 Jun 12;109(24):9557-62</Citation><ArticleIdList><ArticleId IdType="pubmed">22645356</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 Mar 26;382(13):1199-1207</Citation><ArticleIdList><ArticleId IdType="pubmed">31995857</ArticleId></ArticleIdList></Reference><Reference><Citation>Epidemiol Health. 2020;42:e2020007</Citation><ArticleIdList><ArticleId IdType="pubmed">32035431</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Corée du Sud</li></country></list><tree><country name="Corée du Sud"><noRegion><name sortKey="Son, Woo Sik" sort="Son, Woo Sik" uniqKey="Son W" first="Woo-Sik" last="Son">Woo-Sik Son</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Wicri/explor/CovidPublicV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000135 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000135 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Wicri
|area= CovidPublicV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= pubmed:32580535
|texte= Individual-based simulation model for COVID-19 transmission in Daegu, Korea.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:32580535" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a CovidPublicV1
| This area was generated with Dilib version V0.6.38. |  |