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A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions

Epicentres are the focus of COVID‐19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi‐regio...

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Autores principales: Zheng, Qinyue, Wang, Xinwei, Bao, Chunbing, Ji, Yunpeng, Liu, Hua, Meng, Qingchun, Pan, Qiuwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8014041/
https://www.ncbi.nlm.nih.gov/pubmed/33539678
http://dx.doi.org/10.1111/tbed.14019
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author Zheng, Qinyue
Wang, Xinwei
Bao, Chunbing
Ji, Yunpeng
Liu, Hua
Meng, Qingchun
Pan, Qiuwei
author_facet Zheng, Qinyue
Wang, Xinwei
Bao, Chunbing
Ji, Yunpeng
Liu, Hua
Meng, Qingchun
Pan, Qiuwei
author_sort Zheng, Qinyue
collection PubMed
description Epicentres are the focus of COVID‐19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi‐regional, hierarchical‐tier mathematical model for better understanding the complexity and heterogeneity of COVID‐19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi‐regional, hierarchical‐tier SLIHR model. With this model, we revealed insight into how COVID‐19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi‐regional, hierarchical‐tier SLIHR model revealed insight into how COVID‐19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID‐19 pandemic.
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spelling pubmed-80140412021-04-01 A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions Zheng, Qinyue Wang, Xinwei Bao, Chunbing Ji, Yunpeng Liu, Hua Meng, Qingchun Pan, Qiuwei Transbound Emerg Dis Original Articles Epicentres are the focus of COVID‐19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi‐regional, hierarchical‐tier mathematical model for better understanding the complexity and heterogeneity of COVID‐19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi‐regional, hierarchical‐tier SLIHR model. With this model, we revealed insight into how COVID‐19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi‐regional, hierarchical‐tier SLIHR model revealed insight into how COVID‐19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID‐19 pandemic. John Wiley and Sons Inc. 2021-02-21 2022-03 /pmc/articles/PMC8014041/ /pubmed/33539678 http://dx.doi.org/10.1111/tbed.14019 Text en © 2021 The Authors. Transboundary and Emerging Diseases published by Wiley‐VCH GmbH. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Articles
Zheng, Qinyue
Wang, Xinwei
Bao, Chunbing
Ji, Yunpeng
Liu, Hua
Meng, Qingchun
Pan, Qiuwei
A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions
title A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions
title_full A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions
title_fullStr A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions
title_full_unstemmed A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions
title_short A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicentre to adjacent regions
title_sort multi‐regional, hierarchical‐tier mathematical model of the spread and control of covid‐19 epidemics from epicentre to adjacent regions
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8014041/
https://www.ncbi.nlm.nih.gov/pubmed/33539678
http://dx.doi.org/10.1111/tbed.14019
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