Cargando…

A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China

The fourth outbreak of the Coronaviruses, known as the COVID-19, has occurred in Wuhan city of Hubei province in China in December 2019. We propose a time-varying sparse vector autoregressive (VAR) model to retrospectively analyze and visualize the dynamic transmission routes of this outbreak in mai...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Xiandeng, Chang, Le, Shi, Yanlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438497/
https://www.ncbi.nlm.nih.gov/pubmed/32814822
http://dx.doi.org/10.1038/s41598-020-71023-9
_version_ 1783572801878228992
author Jiang, Xiandeng
Chang, Le
Shi, Yanlin
author_facet Jiang, Xiandeng
Chang, Le
Shi, Yanlin
author_sort Jiang, Xiandeng
collection PubMed
description The fourth outbreak of the Coronaviruses, known as the COVID-19, has occurred in Wuhan city of Hubei province in China in December 2019. We propose a time-varying sparse vector autoregressive (VAR) model to retrospectively analyze and visualize the dynamic transmission routes of this outbreak in mainland China over January 31–February 19, 2020. Our results demonstrate that the influential inter-location routes from Hubei have become unidentifiable since February 4, 2020, whereas the self-transmission in each provincial-level administrative region (location, hereafter) was accelerating over February 4–15, 2020. From February 16, 2020, all routes became less detectable, and no influential transmissions could be identified on February 18 and 19, 2020. Such evidence supports the effectiveness of government interventions, including the travel restrictions in Hubei. Implications of our results suggest that in addition to the origin of the outbreak, virus preventions are of crucial importance in locations with the largest migrant workers percentages (e.g., Jiangxi, Henan and Anhui) to controlling the spread of COVID-19.
format Online
Article
Text
id pubmed-7438497
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-74384972020-08-21 A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China Jiang, Xiandeng Chang, Le Shi, Yanlin Sci Rep Article The fourth outbreak of the Coronaviruses, known as the COVID-19, has occurred in Wuhan city of Hubei province in China in December 2019. We propose a time-varying sparse vector autoregressive (VAR) model to retrospectively analyze and visualize the dynamic transmission routes of this outbreak in mainland China over January 31–February 19, 2020. Our results demonstrate that the influential inter-location routes from Hubei have become unidentifiable since February 4, 2020, whereas the self-transmission in each provincial-level administrative region (location, hereafter) was accelerating over February 4–15, 2020. From February 16, 2020, all routes became less detectable, and no influential transmissions could be identified on February 18 and 19, 2020. Such evidence supports the effectiveness of government interventions, including the travel restrictions in Hubei. Implications of our results suggest that in addition to the origin of the outbreak, virus preventions are of crucial importance in locations with the largest migrant workers percentages (e.g., Jiangxi, Henan and Anhui) to controlling the spread of COVID-19. Nature Publishing Group UK 2020-08-19 /pmc/articles/PMC7438497/ /pubmed/32814822 http://dx.doi.org/10.1038/s41598-020-71023-9 Text en © The Author(s) 2020 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/.
spellingShingle Article
Jiang, Xiandeng
Chang, Le
Shi, Yanlin
A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China
title A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China
title_full A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China
title_fullStr A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China
title_full_unstemmed A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China
title_short A retrospective analysis of the dynamic transmission routes of the COVID-19 in mainland China
title_sort retrospective analysis of the dynamic transmission routes of the covid-19 in mainland china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438497/
https://www.ncbi.nlm.nih.gov/pubmed/32814822
http://dx.doi.org/10.1038/s41598-020-71023-9
work_keys_str_mv AT jiangxiandeng aretrospectiveanalysisofthedynamictransmissionroutesofthecovid19inmainlandchina
AT changle aretrospectiveanalysisofthedynamictransmissionroutesofthecovid19inmainlandchina
AT shiyanlin aretrospectiveanalysisofthedynamictransmissionroutesofthecovid19inmainlandchina
AT jiangxiandeng retrospectiveanalysisofthedynamictransmissionroutesofthecovid19inmainlandchina
AT changle retrospectiveanalysisofthedynamictransmissionroutesofthecovid19inmainlandchina
AT shiyanlin retrospectiveanalysisofthedynamictransmissionroutesofthecovid19inmainlandchina