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Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models
BACKGROUND: There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion th...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9701379/ https://www.ncbi.nlm.nih.gov/pubmed/36435792 http://dx.doi.org/10.1186/s40249-022-01039-y |
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author | Rui, Jia Zheng, Jin-Xin Chen, Jin Wei, Hongjie Yu, Shanshan Zhao, Zeyu Wang, Xin-Yi Chen, Mu-Xin Xia, Shang Zhou, Ying Chen, Tianmu Zhou, Xiao-Nong |
author_facet | Rui, Jia Zheng, Jin-Xin Chen, Jin Wei, Hongjie Yu, Shanshan Zhao, Zeyu Wang, Xin-Yi Chen, Mu-Xin Xia, Shang Zhou, Ying Chen, Tianmu Zhou, Xiao-Nong |
author_sort | Rui, Jia |
collection | PubMed |
description | BACKGROUND: There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory. METHODS: Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk. RESULTS: Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81–303.84) and the actual number of the spillovers (0.94–74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value. CONCLUSIONS: Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-022-01039-y. |
format | Online Article Text |
id | pubmed-9701379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97013792022-11-28 Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models Rui, Jia Zheng, Jin-Xin Chen, Jin Wei, Hongjie Yu, Shanshan Zhao, Zeyu Wang, Xin-Yi Chen, Mu-Xin Xia, Shang Zhou, Ying Chen, Tianmu Zhou, Xiao-Nong Infect Dis Poverty Research Article BACKGROUND: There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory. METHODS: Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk. RESULTS: Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81–303.84) and the actual number of the spillovers (0.94–74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value. CONCLUSIONS: Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-022-01039-y. BioMed Central 2022-11-26 /pmc/articles/PMC9701379/ /pubmed/36435792 http://dx.doi.org/10.1186/s40249-022-01039-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Rui, Jia Zheng, Jin-Xin Chen, Jin Wei, Hongjie Yu, Shanshan Zhao, Zeyu Wang, Xin-Yi Chen, Mu-Xin Xia, Shang Zhou, Ying Chen, Tianmu Zhou, Xiao-Nong Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models |
title | Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models |
title_full | Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models |
title_fullStr | Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models |
title_full_unstemmed | Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models |
title_short | Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models |
title_sort | optimal control strategies of sars-cov-2 omicron supported by invasive and dynamic models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9701379/ https://www.ncbi.nlm.nih.gov/pubmed/36435792 http://dx.doi.org/10.1186/s40249-022-01039-y |
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