論文

国際誌
2020年9月23日

Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study.

Journal of clinical medicine
  • Katsuma Hayashi
  • ,
  • Taishi Kayano
  • ,
  • Sumire Sorano
  • ,
  • Hiroshi Nishiura

9
10
開始ページ
3065
終了ページ
3065
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.3390/jcm9103065
出版者・発行元
MDPI AG

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.

リンク情報
DOI
https://doi.org/10.3390/jcm9103065
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/32977578
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598167
URL
https://www.mdpi.com/2077-0383/9/10/3065/pdf
ID情報
  • DOI : 10.3390/jcm9103065
  • eISSN : 2077-0383
  • PubMed ID : 32977578
  • PubMed Central 記事ID : PMC7598167

エクスポート
BibTeX RIS