Cargando…
Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model
The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique fe...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725788/ https://www.ncbi.nlm.nih.gov/pubmed/33298986 http://dx.doi.org/10.1038/s41598-020-76630-0 |
_version_ | 1783620774566821888 |
---|---|
author | Zhang, Yuan You, Chong Cai, Zhenhao Sun, Jiarui Hu, Wenjie Zhou, Xiao-Hua |
author_facet | Zhang, Yuan You, Chong Cai, Zhenhao Sun, Jiarui Hu, Wenjie Zhou, Xiao-Hua |
author_sort | Zhang, Yuan |
collection | PubMed |
description | The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing dynamic models. In this paper, a novel stochastic model was proposed aiming to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We found that: (1) instead of aberration, there was a remarkable amount of asymptomatic virus carriers, (2) a virus carrier with symptoms was approximately twice more likely to pass the disease to others than that of an asymptomatic virus carrier, (3) the transmission rate reduced significantly since the implementation of control measures in Mainland China, and (4) it was expected that the epidemic outbreak would be contained by early March in the selected provinces and cities in China. |
format | Online Article Text |
id | pubmed-7725788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77257882020-12-14 Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model Zhang, Yuan You, Chong Cai, Zhenhao Sun, Jiarui Hu, Wenjie Zhou, Xiao-Hua Sci Rep Article The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing dynamic models. In this paper, a novel stochastic model was proposed aiming to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We found that: (1) instead of aberration, there was a remarkable amount of asymptomatic virus carriers, (2) a virus carrier with symptoms was approximately twice more likely to pass the disease to others than that of an asymptomatic virus carrier, (3) the transmission rate reduced significantly since the implementation of control measures in Mainland China, and (4) it was expected that the epidemic outbreak would be contained by early March in the selected provinces and cities in China. Nature Publishing Group UK 2020-12-09 /pmc/articles/PMC7725788/ /pubmed/33298986 http://dx.doi.org/10.1038/s41598-020-76630-0 Text en © The Author(s) 2020 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 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 Zhang, Yuan You, Chong Cai, Zhenhao Sun, Jiarui Hu, Wenjie Zhou, Xiao-Hua Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model |
title | Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model |
title_full | Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model |
title_fullStr | Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model |
title_full_unstemmed | Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model |
title_short | Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model |
title_sort | prediction of the covid-19 outbreak in china based on a new stochastic dynamic model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725788/ https://www.ncbi.nlm.nih.gov/pubmed/33298986 http://dx.doi.org/10.1038/s41598-020-76630-0 |
work_keys_str_mv | AT zhangyuan predictionofthecovid19outbreakinchinabasedonanewstochasticdynamicmodel AT youchong predictionofthecovid19outbreakinchinabasedonanewstochasticdynamicmodel AT caizhenhao predictionofthecovid19outbreakinchinabasedonanewstochasticdynamicmodel AT sunjiarui predictionofthecovid19outbreakinchinabasedonanewstochasticdynamicmodel AT huwenjie predictionofthecovid19outbreakinchinabasedonanewstochasticdynamicmodel AT zhouxiaohua predictionofthecovid19outbreakinchinabasedonanewstochasticdynamicmodel |