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Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study
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 re...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598167/ https://www.ncbi.nlm.nih.gov/pubmed/32977578 http://dx.doi.org/10.3390/jcm9103065 |
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author | Hayashi, Katsuma Kayano, Taishi Sorano, Sumire Nishiura, Hiroshi |
author_facet | Hayashi, Katsuma Kayano, Taishi Sorano, Sumire Nishiura, Hiroshi |
author_sort | Hayashi, Katsuma |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7598167 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75981672020-10-31 Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study Hayashi, Katsuma Kayano, Taishi Sorano, Sumire Nishiura, Hiroshi J Clin Med Article 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. MDPI 2020-09-23 /pmc/articles/PMC7598167/ /pubmed/32977578 http://dx.doi.org/10.3390/jcm9103065 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hayashi, Katsuma Kayano, Taishi Sorano, Sumire Nishiura, Hiroshi Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study |
title | Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study |
title_full | Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study |
title_fullStr | Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study |
title_full_unstemmed | Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study |
title_short | Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study |
title_sort | hospital caseload demand in the presence of interventions during the covid-19 pandemic: a modeling study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598167/ https://www.ncbi.nlm.nih.gov/pubmed/32977578 http://dx.doi.org/10.3390/jcm9103065 |
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