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Modeling transmission of SARS-CoV-2 Omicron in China

Having adopted a dynamic zero-COVID strategy to respond to SARS-CoV-2 variants with higher transmissibility since August 2021, China is now considering whether, and for how long, this policy can remain in place. The debate has thus shifted towards the identification of mitigation strategies for mini...

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Autores principales: Cai, Jun, Deng, Xiaowei, Yang, Juan, Sun, Kaiyuan, Liu, Hengcong, Chen, Zhiyuan, Peng, Cheng, Chen, Xinhua, Wu, Qianhui, Zou, Junyi, Sun, Ruijia, Zheng, Wen, Zhao, Zeyao, Lu, Wanying, Liang, Yuxia, Zhou, Xiaoyu, Ajelli, Marco, Yu, Hongjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307473/
https://www.ncbi.nlm.nih.gov/pubmed/35537471
http://dx.doi.org/10.1038/s41591-022-01855-7
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author Cai, Jun
Deng, Xiaowei
Yang, Juan
Sun, Kaiyuan
Liu, Hengcong
Chen, Zhiyuan
Peng, Cheng
Chen, Xinhua
Wu, Qianhui
Zou, Junyi
Sun, Ruijia
Zheng, Wen
Zhao, Zeyao
Lu, Wanying
Liang, Yuxia
Zhou, Xiaoyu
Ajelli, Marco
Yu, Hongjie
author_facet Cai, Jun
Deng, Xiaowei
Yang, Juan
Sun, Kaiyuan
Liu, Hengcong
Chen, Zhiyuan
Peng, Cheng
Chen, Xinhua
Wu, Qianhui
Zou, Junyi
Sun, Ruijia
Zheng, Wen
Zhao, Zeyao
Lu, Wanying
Liang, Yuxia
Zhou, Xiaoyu
Ajelli, Marco
Yu, Hongjie
author_sort Cai, Jun
collection PubMed
description Having adopted a dynamic zero-COVID strategy to respond to SARS-CoV-2 variants with higher transmissibility since August 2021, China is now considering whether, and for how long, this policy can remain in place. The debate has thus shifted towards the identification of mitigation strategies for minimizing disruption to the healthcare system in the case of a nationwide epidemic. To this aim, we developed an age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model of SARS-CoV-2 transmission calibrated on the initial growth phase for the 2022 Omicron outbreak in Shanghai, to project COVID-19 burden (that is, number of cases, patients requiring hospitalization and intensive care, and deaths) under hypothetical mitigation scenarios. The model also considers age-specific vaccine coverage data, vaccine efficacy against different clinical endpoints, waning of immunity, different antiviral therapies and nonpharmaceutical interventions. We find that the level of immunity induced by the March 2022 vaccination campaign would be insufficient to prevent an Omicron wave that would result in exceeding critical care capacity with a projected intensive care unit peak demand of 15.6 times the existing capacity and causing approximately 1.55 million deaths. However, we also estimate that protecting vulnerable individuals by ensuring accessibility to vaccines and antiviral therapies, and maintaining implementation of nonpharmaceutical interventions could be sufficient to prevent overwhelming the healthcare system, suggesting that these factors should be points of emphasis in future mitigation policies.
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spelling pubmed-93074732022-07-24 Modeling transmission of SARS-CoV-2 Omicron in China Cai, Jun Deng, Xiaowei Yang, Juan Sun, Kaiyuan Liu, Hengcong Chen, Zhiyuan Peng, Cheng Chen, Xinhua Wu, Qianhui Zou, Junyi Sun, Ruijia Zheng, Wen Zhao, Zeyao Lu, Wanying Liang, Yuxia Zhou, Xiaoyu Ajelli, Marco Yu, Hongjie Nat Med Article Having adopted a dynamic zero-COVID strategy to respond to SARS-CoV-2 variants with higher transmissibility since August 2021, China is now considering whether, and for how long, this policy can remain in place. The debate has thus shifted towards the identification of mitigation strategies for minimizing disruption to the healthcare system in the case of a nationwide epidemic. To this aim, we developed an age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model of SARS-CoV-2 transmission calibrated on the initial growth phase for the 2022 Omicron outbreak in Shanghai, to project COVID-19 burden (that is, number of cases, patients requiring hospitalization and intensive care, and deaths) under hypothetical mitigation scenarios. The model also considers age-specific vaccine coverage data, vaccine efficacy against different clinical endpoints, waning of immunity, different antiviral therapies and nonpharmaceutical interventions. We find that the level of immunity induced by the March 2022 vaccination campaign would be insufficient to prevent an Omicron wave that would result in exceeding critical care capacity with a projected intensive care unit peak demand of 15.6 times the existing capacity and causing approximately 1.55 million deaths. However, we also estimate that protecting vulnerable individuals by ensuring accessibility to vaccines and antiviral therapies, and maintaining implementation of nonpharmaceutical interventions could be sufficient to prevent overwhelming the healthcare system, suggesting that these factors should be points of emphasis in future mitigation policies. Nature Publishing Group US 2022-05-10 2022 /pmc/articles/PMC9307473/ /pubmed/35537471 http://dx.doi.org/10.1038/s41591-022-01855-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cai, Jun
Deng, Xiaowei
Yang, Juan
Sun, Kaiyuan
Liu, Hengcong
Chen, Zhiyuan
Peng, Cheng
Chen, Xinhua
Wu, Qianhui
Zou, Junyi
Sun, Ruijia
Zheng, Wen
Zhao, Zeyao
Lu, Wanying
Liang, Yuxia
Zhou, Xiaoyu
Ajelli, Marco
Yu, Hongjie
Modeling transmission of SARS-CoV-2 Omicron in China
title Modeling transmission of SARS-CoV-2 Omicron in China
title_full Modeling transmission of SARS-CoV-2 Omicron in China
title_fullStr Modeling transmission of SARS-CoV-2 Omicron in China
title_full_unstemmed Modeling transmission of SARS-CoV-2 Omicron in China
title_short Modeling transmission of SARS-CoV-2 Omicron in China
title_sort modeling transmission of sars-cov-2 omicron in china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307473/
https://www.ncbi.nlm.nih.gov/pubmed/35537471
http://dx.doi.org/10.1038/s41591-022-01855-7
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