•  
  •  
 

Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

COVID-19; big data; Wuhan; Beijing

Document Type

Article

Abstract

In December 2019, COVID-19 appeared and started transmission in local population in Wuhan, Hubei Province. We analyzed the spread process of COVID-19 and found that imported number of passengers from Wuhan before the city closure is the main threat to other cities in China, whereas later on local transmission in those cities gradually become the main force of virus transmission. Based on SEIR model, we found that the basic reproductive number R 0 for Wuhan is much higher than that of Beijing. When pandemic control measures (traffic control, holiday extension, 14-day-long quarantine, etc.) are taken into account, the R 0 dropped substantially. China's progressive pandemic control policy ensures the situation under control, and the timely situation reporting and data sharing greatly contribute to the whole world fighting against this novel coronavirus.

First page

248

Last Page

255

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

Xu X, Chen P, Wang J, et al. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Science China Life Sciences, 2020, DOI:10.1007/s11427-020-1637-5.

Ji W, Wang W, Zhao X, et al. Homologous recombination within the spike glycoprotein of the newly identified coronavirus may boost cross-species transmission from snake to human. Journal of Medical Virology, 2020, DOI:10.1002/jmv.25682.

Zhou P, Yang X L, Wang X G, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 2020, DOI:10.1038/s41586-020-2012-7.

Benvenuto D, Giovannetti M, Ciccozzi A, et al. The 2019-new coronavirus epidemic:Evidence for virus evolution. Journal of Medical Virology, 2020, DOI:10.1002/jmv.25688.

Wu F, Zhao S, Yu B, et al. Complete genome characterisation of a novel coronavirus associated with severe human respiratory disease in Wuhan, China. bioRxiv, 2020, DOI:10.1101/2020.01.24.919183.

Paraskevis D, Kostaki E G, Magiorkinis G, et al. Full-genome evolutionary analysis of the novel corona virus (2019-nCoV) rejects the hypothesis of emergence as a result of a recent recombination event. Infection, Genetics and Evolution, 2020, 79:104212. DOI:10.1016/j.meegid.2020.104212.

Letko M, Munster V. Functional assessment of cell entry and receptor usage for lineage B β-coronaviruses, including 2019-nCoV. bioRxiv, 2020, DOI:10.1101/2020.01.22.915660.

Xu Z, Peng C, Shi Y, et al. Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation. bioRxiv, 2020, DOI:10.1101/2020.01.27.921627.

Tian X, Li C, Huang A, et al. Potent binding of 2019 novel coronavirus spike protein by a SARS coronavirusspecific human monoclonal antibody. bioRxiv, 2020, DOI:10.1101/2020.01.28.923011.

Guo Q, Li M, Wang C et al. Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm. bioRxiv, 2020, DOI:10.1101/2020.01.21.914044.

Chen T, Rui J, Wang Q, et al. A mathematical model for simulating the transmission of Wuhan novel Coronavirus. bioRxiv, 2020, DOI:10.1101/2020.01.19.911669.

Riou J, Althaus C L. Pattern of early human-to-human transmission of Wuhan 2019-nCoV. bioRxiv, 2020, DOI:10.1101/2020.01.23.917351.

Zhao S, Lin Q, Ran J, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020:A data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases, 2020, DOI:10.1016/j.ijid.2020.01.050.

Shen M, Peng Z, Xiao Y, et al. Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China. bioRxiv, 2020, DOI:10.1101/2020.01.23.916726.

Chen Z, Zhang W, Lu Y, et al. From SARS-CoV to Wuhan 2019-nCoV outbreak:Similarity of early epidemic and prediction of future trend. bioRxiv, 2020, DOI:10.1101/2020.01.24.919241.

Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. New England Journal of Medicine, 2020, DOI:10.1056/NEJMoa2001017.

Liu T, Hu J, Kang M, et al. Transmission dynamics of 2019 novel coronavirus (2019-nCoV). bioRxiv, 2020, DOI:10.1101/2020.01.25.919787.

Zhang C, Wang M. Origin time and epidemic dynamics of the 2019 novel coronavirus. bioRxiv, 2020, DOI:10.1101/2020.01.25.919688.

Ming W, Huang J, Zhang C J P. Breaking down of healthcare system:Mathematical modelling for controlling the novel coronavirus (2019-nCoV) outbreak in Wuhan, China. bioRxiv, 2020, DOI:10.1101/2020.01.27.922443.

Shao P, Shan Y. Beware of asymptomatic transmission:Study on 2019-nCoV prevention and control measures based on extended SEIR model. bioRxiv, 2020, DOI:10.1101/2020.01.28.923169.

Hethcote H W. The mathematics of infectious diseases. Siam Review, 2000, 42:599-653.

Wu J T, Leung K, Leung G M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China:A modelling study. The Lancet, 2020, DOI:10.1016/S0140-6736(20)30260-9.

Read J M, Bridgen J R, Cummings D A, et al. Novel coronavirus 2019-nCoV:early estimation of epidemiological parameters and epidemic predictions. medRxiv, 2020, DOI:10.1101/2020.01.23.20018549.

Eiser J R, Stafford T, Henneberry J, et al. "Trust me, I'm a Scientist (Not a Developer)":Perceived expertise and motives as predictors of trust in assessment of risk from contaminated land. Risk Analysis, 2009, 29(2):288-297.

赵斌.疫情高发, 大众为什么还不信任科学家?[2020-02-10]. http://blog.sciencenet.cn/blog-502444-1217726.html.

陈安.对当前肺炎疫情应急的八条建议.[2020-02-10]. http://blog.sciencenet.cn/blog-53483-1216183.html.

Download
Request a Copy

Share

COinS