Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study.
Identifieur interne : 000150 ( Main/Exploration ); précédent : 000149; suivant : 000151Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study.
Auteurs : Katsuma Hayashi [Japon] ; Taishi Kayano [Japon] ; Sumire Sorano [Royaume-Uni] ; Hiroshi Nishiura [Japon]Source :
- Journal of clinical medicine [ 2077-0383 ] ; 2020.
Abstract
A surge in hospital admissions was observed in Japan in late March 2020, and the incidence of coronavirus disease (COVID-19) temporarily reduced from March to May as a result of the closure of host and hostess clubs, shortening the opening hours of bars and restaurants, and requesting a voluntary reduction of contact outside the household. To prepare for the second wave, it is vital to anticipate caseload demand, and thus, the number of required hospital beds for admitted cases and plan interventions through scenario analysis. In the present study, we analyzed the first wave data by age group so that the age-specific number of hospital admissions could be projected for the second wave. Because the age-specific patterns of the epidemic were different between urban and other areas, we analyzed datasets from two distinct cities: Osaka, where the cases were dominated by young adults, and Hokkaido, where the older adults accounted for the majority of hospitalized cases. By estimating the exponential growth rates of cases by age group and assuming probable reductions in those rates under interventions, we obtained projected epidemic curves of cases in addition to hospital admissions. We demonstrated that the longer our interventions were delayed, the higher the peak of hospital admissions. Although the approach relies on a simplistic model, the proposed framework can guide local government to secure the essential number of hospital beds for COVID-19 cases and formulate action plans.
DOI: 10.3390/jcm9103065
PubMed: 32977578
PubMed Central: PMC7598167
Affiliations:
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type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A surge in hospital admissions was observed in Japan in late March 2020, and the incidence of coronavirus disease (COVID-19) temporarily reduced from March to May as a result of the closure of host and hostess clubs, shortening the opening hours of bars and restaurants, and requesting a voluntary reduction of contact outside the household. To prepare for the second wave, it is vital to anticipate caseload demand, and thus, the number of required hospital beds for admitted cases and plan interventions through scenario analysis. In the present study, we analyzed the first wave data by age group so that the age-specific number of hospital admissions could be projected for the second wave. Because the age-specific patterns of the epidemic were different between urban and other areas, we analyzed datasets from two distinct cities: Osaka, where the cases were dominated by young adults, and Hokkaido, where the older adults accounted for the majority of hospitalized cases. By estimating the exponential growth rates of cases by age group and assuming probable reductions in those rates under interventions, we obtained projected epidemic curves of cases in addition to hospital admissions. We demonstrated that the longer our interventions were delayed, the higher the peak of hospital admissions. Although the approach relies on a simplistic model, the proposed framework can guide local government to secure the essential number of hospital beds for COVID-19 cases and formulate action plans.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32977578</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>03</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Print">2077-0383</ISSN><JournalIssue CitedMedium="Print"><Volume>9</Volume><Issue>10</Issue><PubDate><Year>2020</Year><Month>Sep</Month><Day>23</Day></PubDate></JournalIssue><Title>Journal of clinical medicine</Title><ISOAbbreviation>J Clin Med</ISOAbbreviation></Journal><ArticleTitle>Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">E3065</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.3390/jcm9103065</ELocationID><Abstract><AbstractText>A surge in hospital admissions was observed in Japan in late March 2020, and the incidence of coronavirus disease (COVID-19) temporarily reduced from March to May as a result of the closure of host and hostess clubs, shortening the opening hours of bars and restaurants, and requesting a voluntary reduction of contact outside the household. To prepare for the second wave, it is vital to anticipate caseload demand, and thus, the number of required hospital beds for admitted cases and plan interventions through scenario analysis. In the present study, we analyzed the first wave data by age group so that the age-specific number of hospital admissions could be projected for the second wave. Because the age-specific patterns of the epidemic were different between urban and other areas, we analyzed datasets from two distinct cities: Osaka, where the cases were dominated by young adults, and Hokkaido, where the older adults accounted for the majority of hospitalized cases. By estimating the exponential growth rates of cases by age group and assuming probable reductions in those rates under interventions, we obtained projected epidemic curves of cases in addition to hospital admissions. We demonstrated that the longer our interventions were delayed, the higher the peak of hospital admissions. Although the approach relies on a simplistic model, the proposed framework can guide local government to secure the essential number of hospital beds for COVID-19 cases and formulate action plans.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hayashi</LastName><ForeName>Katsuma</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Kyoto University School of Public Health, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kayano</LastName><ForeName>Taishi</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Kyoto University School of Public Health, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sorano</LastName><ForeName>Sumire</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7H, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nishiura</LastName><ForeName>Hiroshi</ForeName><Initials>H</Initials><Identifier Source="ORCID">0000-0003-0941-8537</Identifier><AffiliationInfo><Affiliation>Kyoto University School of Public Health, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama 332-0012, Japan.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>PI: Tomoya Saito, PI: Motoi Suzuki</GrantID><Agency>Health and Labour Sciences Research Grant</Agency><Country></Country></Grant><Grant><GrantID>PI: Tadaki Suzuki, PI: Hiroshi Nishiura</GrantID><Agency>Japan Agency for Medical Research and Development</Agency><Country></Country></Grant><Grant><GrantID>17H04701</GrantID><Agency>Japan Society for the Promotion of Science</Agency><Country></Country></Grant><Grant><GrantID>JPMJCR1413</GrantID><Agency>Core Research for Evolutional Science and Technology</Agency><Country></Country></Grant><Grant><GrantID>NA</GrantID><Agency>Inamori Foundation</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>23</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">epidemiology</Keyword><Keyword MajorTopicYN="N">hospitalization</Keyword><Keyword MajorTopicYN="N">mathematical model</Keyword><Keyword MajorTopicYN="N">policy making</Keyword><Keyword 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sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name><name sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name></country><country name="Royaume-Uni"><noRegion><name sortKey="Sorano, Sumire" sort="Sorano, Sumire" uniqKey="Sorano S" first="Sumire" last="Sorano">Sumire Sorano</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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