Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model.
Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model.
复制标题
基于新随机动态模型的中国新冠肺炎疫情预测
DOI：
10.1038/s41598-020-76630-0
复制
发表时间：
2020-12-09
影响因子：
4.6
通讯作者：
Zhou XH
中科院分区：
文献类型：
--
作者：
Zhang Y;You C;Cai Z;Sun J;Hu W;Zhou XH
目前爆发的2019冠状病毒病（COVID-19）因其在全球范围内的快速和广泛传播而成为全球危机。充分了解疾病的动态将大大加强COVID 19的控制和预防。然而，据我们所知，疫情的独特特征限制了所有现有动态模型的应用。本文提出了一个新的随机模型，旨在解释COVID-19的独特传播动态，并捕捉中国大陆实施的干预措施的效果。我们发现：（1）无症状病毒携带者的数量显著增加，而不是变异;（2）有症状的病毒携带者将疾病传播给他人的可能性比无症状病毒携带者高出约两倍;（3）自中国大陆实施控制措施以来，传播率显著降低，及（4）预期疫情可于3月初在中国选定省市得到控制。
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.
登录
查看更多内容
DOI：
10.1098/rspa.1927.0118
发表时间：
1927-08-01
期刊：
PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES A-CONTAINING PAPERS OF A MATHEMATICAL AND PHYSICAL CHARACTER
影响因子：
--
作者：
Kermack, WO;McKendrick, AG
通讯作者：
McKendrick, AG
影响因子：
56.9
作者：
Chinazzi, Matteo;Davis, Jessica T.;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
56.9
作者：
Riley, S;Fraser, C;Anderson, RM
通讯作者：
Anderson, RM
影响因子：
56.3
作者：
Kucharski, Adam J.;Russell, Timothy W.;Eggo, Rosalind M.
通讯作者：
Eggo, Rosalind M.
影响因子：
1.6
作者：
Liu, Yingnan;Jiang, Xingyuan;Tao, Xia
通讯作者：
Tao, Xia