Analysis of COVID-19 infection spread in Japan based on stochastic transition model.
Identifieur interne : 000321 ( PubMed/Curation ); précédent : 000320; suivant : 000322Analysis of COVID-19 infection spread in Japan based on stochastic transition model.
Auteurs : Kenji Karako [Japon] ; Peipei Song [Japon] ; Yu Chen [Japon] ; Wei Tang [Japon]Source :
- Bioscience trends [ 1881-7823 ] ; 2020.
Abstract
To assess the effectiveness of response strategies of avoiding large gatherings or crowded areas and to predict the spread of COVID-19 infections in Japan, we developed a stochastic transmission model by extending the Susceptible-Infected-Removed (SIR) epidemiological model with an additional modeling of the individual action on whether to stay away from the crowded areas. The population were divided into three compartments: Susceptible, Infected, Removed. Susceptible transitions to Infected every hour with a probability determined by the ratio of Infected and the congestion of area. The total area consists of three zones crowded zone, mid zone and uncrowded zone, with different infection probabilities characterized by the number of people gathered there. The time for each people to spend in the crowded zone is curtailed by 0, 2, 4, 6, 7, and 8 hours, and the time spent in mid zone is extended accordingly. This simulation showed that the number of Infected and Removed will increase rapidly if there is no reduction of the time spent in crowded zone. On the other hand, the stagnant growth of Infected can be observed when the time spent in the crowded zone is reduced to 4 hours, and the growth number of Infected will decrease and the spread of the infection will subside gradually if the time spent in the crowded zone is further cut to 2 hours. In conclusions The infection spread in Japan will be gradually contained by reducing the time spent in the crowded zone to less than 4 hours.
DOI: 10.5582/bst.2020.01482
PubMed: 32188819
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Medicine, Tokyo, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Institute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo</wicri:regionArea></affiliation></author><author><name sortKey="Chen, Yu" sort="Chen, Yu" uniqKey="Chen Y" first="Yu" last="Chen">Yu Chen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba</wicri:regionArea></affiliation></author><author><name sortKey="Tang, Wei" sort="Tang, Wei" uniqKey="Tang W" first="Wei" last="Tang">Wei Tang</name><affiliation wicri:level="1"><nlm:affiliation>International Health Care Center, 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uniqKey="Karako K" first="Kenji" last="Karako">Kenji Karako</name><affiliation wicri:level="1"><nlm:affiliation>Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba</wicri:regionArea></affiliation></author><author><name sortKey="Song, Peipei" sort="Song, Peipei" uniqKey="Song P" first="Peipei" last="Song">Peipei Song</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Institute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo</wicri:regionArea></affiliation></author><author><name sortKey="Chen, Yu" sort="Chen, Yu" uniqKey="Chen Y" first="Yu" last="Chen">Yu Chen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba</wicri:regionArea></affiliation></author><author><name sortKey="Tang, Wei" sort="Tang, Wei" uniqKey="Tang W" first="Wei" last="Tang">Wei Tang</name><affiliation wicri:level="1"><nlm:affiliation>International Health Care Center, National Center for Global Health and Medicine, Tokyo, Japan.</nlm:affiliation><country xml:lang="fr">Japon</country><wicri:regionArea>International Health Care Center, National Center for Global Health and Medicine, Tokyo</wicri:regionArea></affiliation></author></analytic><series><title level="j">Bioscience trends</title><idno type="eISSN">1881-7823</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">To assess the effectiveness of response strategies of avoiding large gatherings or crowded areas and to predict the spread of COVID-19 infections in Japan, we developed a stochastic transmission model by extending the Susceptible-Infected-Removed (SIR) epidemiological model with an additional modeling of the individual action on whether to stay away from the crowded areas. The population were divided into three compartments: Susceptible, Infected, Removed. Susceptible transitions to Infected every hour with a probability determined by the ratio of Infected and the congestion of area. The total area consists of three zones crowded zone, mid zone and uncrowded zone, with different infection probabilities characterized by the number of people gathered there. The time for each people to spend in the crowded zone is curtailed by 0, 2, 4, 6, 7, and 8 hours, and the time spent in mid zone is extended accordingly. This simulation showed that the number of Infected and Removed will increase rapidly if there is no reduction of the time spent in crowded zone. On the other hand, the stagnant growth of Infected can be observed when the time spent in the crowded zone is reduced to 4 hours, and the growth number of Infected will decrease and the spread of the infection will subside gradually if the time spent in the crowded zone is further cut to 2 hours. In conclusions The infection spread in Japan will be gradually contained by reducing the time spent in the crowded zone to less than 4 hours.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32188819</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1881-7823</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Mar</Month><Day>19</Day></PubDate></JournalIssue><Title>Bioscience trends</Title><ISOAbbreviation>Biosci Trends</ISOAbbreviation></Journal><ArticleTitle>Analysis of COVID-19 infection spread in Japan based on stochastic transition model.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.5582/bst.2020.01482</ELocationID><Abstract><AbstractText>To assess the effectiveness of response strategies of avoiding large gatherings or crowded areas and to predict the spread of COVID-19 infections in Japan, we developed a stochastic transmission model by extending the Susceptible-Infected-Removed (SIR) epidemiological model with an additional modeling of the individual action on whether to stay away from the crowded areas. The population were divided into three compartments: Susceptible, Infected, Removed. Susceptible transitions to Infected every hour with a probability determined by the ratio of Infected and the congestion of area. The total area consists of three zones crowded zone, mid zone and uncrowded zone, with different infection probabilities characterized by the number of people gathered there. The time for each people to spend in the crowded zone is curtailed by 0, 2, 4, 6, 7, and 8 hours, and the time spent in mid zone is extended accordingly. This simulation showed that the number of Infected and Removed will increase rapidly if there is no reduction of the time spent in crowded zone. On the other hand, the stagnant growth of Infected can be observed when the time spent in the crowded zone is reduced to 4 hours, and the growth number of Infected will decrease and the spread of the infection will subside gradually if the time spent in the crowded zone is further cut to 2 hours. In conclusions The infection spread in Japan will be gradually contained by reducing the time spent in the crowded zone to less than 4 hours.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Karako</LastName><ForeName>Kenji</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Song</LastName><ForeName>Peipei</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Institute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Yu</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tang</LastName><ForeName>Wei</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>International Health Care Center, National Center for Global Health and Medicine, Tokyo, Japan.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>03</Month><Day>19</Day></ArticleDate></Article><MedlineJournalInfo><Country>Japan</Country><MedlineTA>Biosci Trends</MedlineTA><NlmUniqueID>101502754</NlmUniqueID><ISSNLinking>1881-7815</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Japan</Keyword><Keyword MajorTopicYN="N">Susceptible-Infected-Removed (SIR)</Keyword><Keyword MajorTopicYN="N">coronavirus disease 2019 (COVID-19)</Keyword><Keyword MajorTopicYN="N">infection</Keyword><Keyword MajorTopicYN="N">modeling</Keyword><Keyword MajorTopicYN="N">transmission</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32188819</ArticleId><ArticleId IdType="doi">10.5582/bst.2020.01482</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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