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Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data
The transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to be overdispersed, meaning that only a fraction of infected cases contributes to super-spreading. While cluster interventions are an effective measure for controlling pandemics due to the viruses’ overdispers...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967753/ https://www.ncbi.nlm.nih.gov/pubmed/36851670 http://dx.doi.org/10.3390/v15020456 |
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author | Ueda, Minami Hayashi, Katsuma Nishiura, Hiroshi |
author_facet | Ueda, Minami Hayashi, Katsuma Nishiura, Hiroshi |
author_sort | Ueda, Minami |
collection | PubMed |
description | The transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to be overdispersed, meaning that only a fraction of infected cases contributes to super-spreading. While cluster interventions are an effective measure for controlling pandemics due to the viruses’ overdispersed nature, a quantitative assessment of the risk of clustering has yet to be sufficiently presented. Using systematically collected cluster surveillance data for coronavirus disease 2019 (COVID-19) from June 2020 to June 2021 in Japan, we estimated the activity-dependent risk of clustering in 23 establishment types. The analysis indicated that elderly care facilities, welfare facilities for people with disabilities, and hospitals had the highest risk of clustering, with 4.65 (95% confidence interval [CI]: 4.43–4.87), 2.99 (2.59–3.46), and 2.00 (1.88–2.12) cluster reports per million event users, respectively. Risks in educational settings were higher overall among older age groups, potentially being affected by activities with close and uncontrollable contact during extracurricular hours. In dining settings, drinking and singing increased the risk by 10- to 70-fold compared with regular eating settings. The comprehensive analysis of the COVID-19 cluster records provides an additional scientific basis for the design of customized interventions. |
format | Online Article Text |
id | pubmed-9967753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99677532023-02-27 Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data Ueda, Minami Hayashi, Katsuma Nishiura, Hiroshi Viruses Article The transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to be overdispersed, meaning that only a fraction of infected cases contributes to super-spreading. While cluster interventions are an effective measure for controlling pandemics due to the viruses’ overdispersed nature, a quantitative assessment of the risk of clustering has yet to be sufficiently presented. Using systematically collected cluster surveillance data for coronavirus disease 2019 (COVID-19) from June 2020 to June 2021 in Japan, we estimated the activity-dependent risk of clustering in 23 establishment types. The analysis indicated that elderly care facilities, welfare facilities for people with disabilities, and hospitals had the highest risk of clustering, with 4.65 (95% confidence interval [CI]: 4.43–4.87), 2.99 (2.59–3.46), and 2.00 (1.88–2.12) cluster reports per million event users, respectively. Risks in educational settings were higher overall among older age groups, potentially being affected by activities with close and uncontrollable contact during extracurricular hours. In dining settings, drinking and singing increased the risk by 10- to 70-fold compared with regular eating settings. The comprehensive analysis of the COVID-19 cluster records provides an additional scientific basis for the design of customized interventions. MDPI 2023-02-06 /pmc/articles/PMC9967753/ /pubmed/36851670 http://dx.doi.org/10.3390/v15020456 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ueda, Minami Hayashi, Katsuma Nishiura, Hiroshi Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data |
title | Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data |
title_full | Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data |
title_fullStr | Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data |
title_full_unstemmed | Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data |
title_short | Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data |
title_sort | identifying high-risk events for covid-19 transmission: estimating the risk of clustering using nationwide data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967753/ https://www.ncbi.nlm.nih.gov/pubmed/36851670 http://dx.doi.org/10.3390/v15020456 |
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