Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020.
Identifieur interne : 000388 ( PubMed/Curation ); précédent : 000387; suivant : 000389Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020.
Auteurs : Toshikazu Kuniya [Japon]Source :
- Journal of clinical medicine [ 2077-0383 ] ; 2020.
Abstract
The first case of coronavirus disease 2019 (COVID-19) in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is R 0 = 2 . 6 ( 95 % CI, 2 . 4 - 2 . 8 ) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population; (2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size.
DOI: 10.3390/jcm9030789
PubMed: 32183172
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The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is R 0 = 2 . 6 ( 95 % CI, 2 . 4 - 2 . 8 ) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population; (2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32183172</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>18</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Print">2077-0383</ISSN><JournalIssue CitedMedium="Print"><Volume>9</Volume><Issue>3</Issue><PubDate><Year>2020</Year><Month>Mar</Month><Day>13</Day></PubDate></JournalIssue><Title>Journal of clinical medicine</Title><ISOAbbreviation>J Clin Med</ISOAbbreviation></Journal><ArticleTitle>Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">E789</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/jcm9030789</ELocationID><Abstract><AbstractText>The first case of coronavirus disease 2019 (COVID-19) in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is R 0 = 2 . 6 ( 95 % CI, 2 . 4 - 2 . 8 ) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population; (2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kuniya</LastName><ForeName>Toshikazu</ForeName><Initials>T</Initials><Identifier Source="ORCID">0000-0002-4239-8059</Identifier><AffiliationInfo><Affiliation>Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>19K14594</GrantID><Agency>Japan Society for the Promotion of Science</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>03</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>J Clin Med</MedlineTA><NlmUniqueID>101606588</NlmUniqueID><ISSNLinking>2077-0383</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">SEIR compartmental model</Keyword><Keyword MajorTopicYN="N">basic reproduction number</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>02</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>03</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>03</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>19</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32183172</ArticleId><ArticleId IdType="pii">jcm9030789</ArticleId><ArticleId IdType="doi">10.3390/jcm9030789</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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