Analysis of COVID-19 infection spread in Japan based on stochastic transition model.
Identifieur interne : 000645 ( new/Analysis ); précédent : 000644; suivant : 000646Analysis 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
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream PubMed, to step Corpus: 000321
- to stream PubMed, to step Curation: 000321
- to stream PubMed, to step Checkpoint: 000B63
- to stream Ncbi, to step Merge: 001277
- to stream Ncbi, to step Curation: 001277
- to stream Ncbi, to step Checkpoint: 001277
- to stream Main, to step Merge: 000C35
- to stream Main, to step Curation: 000C33
- to stream Main, to step Exploration: 000C33
- to stream new, to step Extraction: 000645
Links to Exploration step
pubmed:32188819Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Analysis of COVID-19 infection spread in Japan based on stochastic transition model.</title>
<author><name sortKey="Karako, Kenji" sort="Karako, Kenji" uniqKey="Karako K" first="Kenji" last="Karako">Kenji Karako</name>
<affiliation wicri:level="4"><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>
<orgName type="university">Université de Tokyo</orgName>
<placeName><settlement type="city">Tokyo</settlement>
<region type="province">Région de Kantō</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Song, Peipei" sort="Song, Peipei" uniqKey="Song P" first="Peipei" last="Song">Peipei Song</name>
<affiliation wicri:level="3"><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>
<placeName><settlement type="city">Tokyo</settlement>
<region type="région">Région de Kantō</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Chen, Yu" sort="Chen, Yu" uniqKey="Chen Y" first="Yu" last="Chen">Yu Chen</name>
<affiliation wicri:level="4"><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>
<orgName type="university">Université de Tokyo</orgName>
<placeName><settlement type="city">Tokyo</settlement>
<region type="province">Région de Kantō</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Tang, Wei" sort="Tang, Wei" uniqKey="Tang W" first="Wei" last="Tang">Wei Tang</name>
<affiliation wicri:level="3"><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>
<placeName><settlement type="city">Tokyo</settlement>
<region type="région">Région de Kantō</region>
</placeName>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2020">2020</date>
<idno type="RBID">pubmed:32188819</idno>
<idno type="pmid">32188819</idno>
<idno type="doi">10.5582/bst.2020.01482</idno>
<idno type="wicri:Area/PubMed/Corpus">000321</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000321</idno>
<idno type="wicri:Area/PubMed/Curation">000321</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000321</idno>
<idno type="wicri:Area/PubMed/Checkpoint">000B63</idno>
<idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000B63</idno>
<idno type="wicri:Area/Ncbi/Merge">001277</idno>
<idno type="wicri:Area/Ncbi/Curation">001277</idno>
<idno type="wicri:Area/Ncbi/Checkpoint">001277</idno>
<idno type="wicri:Area/Main/Merge">000C35</idno>
<idno type="wicri:Area/Main/Curation">000C33</idno>
<idno type="wicri:Area/Main/Exploration">000C33</idno>
<idno type="wicri:Area/new/Extraction">000645</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">Analysis of COVID-19 infection spread in Japan based on stochastic transition model.</title>
<author><name sortKey="Karako, Kenji" sort="Karako, Kenji" uniqKey="Karako K" first="Kenji" last="Karako">Kenji Karako</name>
<affiliation wicri:level="4"><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>
<orgName type="university">Université de Tokyo</orgName>
<placeName><settlement type="city">Tokyo</settlement>
<region type="province">Région de Kantō</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Song, Peipei" sort="Song, Peipei" uniqKey="Song P" first="Peipei" last="Song">Peipei Song</name>
<affiliation wicri:level="3"><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>
<placeName><settlement type="city">Tokyo</settlement>
<region type="région">Région de Kantō</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Chen, Yu" sort="Chen, Yu" uniqKey="Chen Y" first="Yu" last="Chen">Yu Chen</name>
<affiliation wicri:level="4"><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>
<orgName type="university">Université de Tokyo</orgName>
<placeName><settlement type="city">Tokyo</settlement>
<region type="province">Région de Kantō</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Tang, Wei" sort="Tang, Wei" uniqKey="Tang W" first="Wei" last="Tang">Wei Tang</name>
<affiliation wicri:level="3"><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>
<placeName><settlement type="city">Tokyo</settlement>
<region type="région">Région de Kantō</region>
</placeName>
</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>
<affiliations><list><country><li>Japon</li>
</country>
<region><li>Région de Kantō</li>
</region>
<settlement><li>Tokyo</li>
</settlement>
<orgName><li>Université de Tokyo</li>
</orgName>
</list>
<tree><country name="Japon"><region name="Région de Kantō"><name sortKey="Karako, Kenji" sort="Karako, Kenji" uniqKey="Karako K" first="Kenji" last="Karako">Kenji Karako</name>
</region>
<name sortKey="Chen, Yu" sort="Chen, Yu" uniqKey="Chen Y" first="Yu" last="Chen">Yu Chen</name>
<name sortKey="Song, Peipei" sort="Song, Peipei" uniqKey="Song P" first="Peipei" last="Song">Peipei Song</name>
<name sortKey="Tang, Wei" sort="Tang, Wei" uniqKey="Tang W" first="Wei" last="Tang">Wei Tang</name>
</country>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Sante/explor/CovidV2/Data/new/Analysis
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000645 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/new/Analysis/biblio.hfd -nk 000645 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Wicri/Sante |area= CovidV2 |flux= new |étape= Analysis |type= RBID |clé= pubmed:32188819 |texte= Analysis of COVID-19 infection spread in Japan based on stochastic transition model. }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/new/Analysis/RBID.i -Sk "pubmed:32188819" \ | HfdSelect -Kh $EXPLOR_AREA/Data/new/Analysis/biblio.hfd \ | NlmPubMed2Wicri -a CovidV2
This area was generated with Dilib version V0.6.33. |