Please wait a minute...
Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

Postal Subscription Code 80-971

Quant. Biol.    2020, Vol. 8 Issue (1) : 11-19    https://doi.org/10.1007/s40484-020-0199-0
RESEARCH ARTICLE
Modeling the epidemic dynamics and control of COVID-19 outbreak in China
Shilei Zhao1,2,3, Hua Chen1,2,3,4()
1. CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
2. China National Center for Bioinformation, Beijing 100101, China
3. School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
4. CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
 Download: PDF(1173 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Background: The coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures.

Methods: We develop a Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models.

Results: The SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number , and after January 30, all regions had a reproductive number , indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of un-quarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty.

Conclusions: We suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran.

Keywords coronavirus disease 2019      SARS-CoV-2      epidemic model     
Corresponding Author(s): Hua Chen   
Just Accepted Date: 06 March 2020   Online First Date: 11 March 2020    Issue Date: 23 March 2020
 Cite this article:   
Shilei Zhao,Hua Chen. Modeling the epidemic dynamics and control of COVID-19 outbreak in China[J]. Quant. Biol., 2020, 8(1): 11-19.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-020-0199-0
https://academic.hep.com.cn/qb/EN/Y2020/V8/I1/11
Fig.1  Inferring the epidemic dynamics in Wuhan.
Wuhan Hubei (excluding Wuhan) China (excluding Hubei)
stage I stage II stage III stage I stage II stage I stage II
Quarantine rate 0.0630 0.3917 0.6185 0.05 0.4880 0.1941 0.5157
Reproductive number 4.7092 0.7575 0.4797 5.934 0.6079 1.5283 0.5753
Confirmation rate 0.05 0.0643 0.3220 0.05 0.1914 0.05 0.2189
End time ( U< 1) 299 101 28 397 47 368 38
End time ( CtCt 1<1) 328 147 33 391 55 477 45
Infected number 8,923, 823 62,557 49, 510 47,971, 179 17,679 802,606, 289 13,322
R2 0.8720 0.9746 0.9961 0.9390 0.9863 0.9863 0.9960
Tab.1  Parameter estimation of the epidemic dynamics in Wuhan, Hubei (excluding Wuhan) and China (excluding Hubei)
Beijing Shanghai Guangzhou Shenzhen
stage I stage II stage I stage II stage I stage II stage I stage II
Quarantine rate 0.3357 0.5647 0.0817 0.5813 0.2467 0.5838 0.05 0.5566
Reproductive number 0.8840 0.5254 3.6288 0.5104 1.2026 0.5082 5.934 0.5331
Confirmation rate 0.0906 0.2680 0.0881 0.2846 0.05 0.2871 0.05 0.2599
End time ( U< 1) 117 19 282 16 481 19 379 17
End time ( CtCt 1<1) 103 23 280 20 492 23 372 21
Infected number 814 388 23,524,072 326 4,724,899 345 12,992,106 411
R2 0.9636 0.9839 0.9760 0.9864 0.9290 0.9821 0.9617 0.9845
Tab.2  Parameter inference of the epidemic dynamics in Beijing, Shanghai, Guangzhou and Shenzhen
Fig.2  Inferring the epidemic dynamics in Hubei province (excluding Wuhan).
Fig.3  Inferring epidemic dynamics in China (excluding Hubei province).
1 Wuhan Municipal Health Commission. Wuhan Municipal Health Commission briefing on the pneumonia epidemic situation (31 Dec 2019, in Chinese).
2 C. Wang, , P. W. Horby, , F. G. Hayden, and G. F. Gao, (2020) A novel coronavirus outbreak of global health concern. Lancet, 395, 470–473
https://doi.org/10.1016/S0140-6736(20)30185-9. pmid: 31986257
3 J. F.-W. Chan, , S. Yuan, , K.-H. Kok, , K. K. To, , H. Chu, , J. Yang, , F. Xing, , J. Liu, , C. C. Yip, , R. W. Poon, , et al. (2020) A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet, 395, 514–523
https://doi.org/10.1016/S0140-6736(20)30154-9. pmid: 31986261
4 National Health Commission of the People’s Republic of China. Infectious Disease Specialists. Prevention and control of novel coronavirus pneumonia (The Fifth Edition), (2020, in Chinese).
5 Z. Du,, L. Wang,, S. Chauchemez,, X. Xu,, X. Wang,, B.J., Cowling, L.A. Meyers, (2020) Risk for transportation of 2019 novel coronavirus disease from Wuhan to other cities in China. Emerg. Infect. Dis.,
https://doi.org/10.3201/eid2605.200146
6 Q. Li,, X. Guan,, P. Wu,, X. Wang,, L. Zhou,, Y. Tong,, R. Ren,, K. Leung,, E. Lau,, J Wong,., et al. (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N. Engl. J. Med., NEJMoa2001316
7 J. Stehlé, , N. Voirin, , A. Barrat, , C. Cattuto,, V. Colizza,, L. Isella,, C. Régis,, J.-F. Pinton,, N. Khanafer,, W. Van den Broeck,, et al.(2011) Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC Med., 9, 87
8 D. Fisman, , E. Khoo, and A. Tuite, (2014) Early epidemic dynamics of the west african 2014 ebola outbreak: estimates derived with a simple two-parameter model. PLoS Curr., 6, 6
https://doi.org/10.1371/currents.outbreaks.89c0d3783f36958d96ebbae97348d571. pmid: 25642358
9 N. Imai, , A. Cori, , I. Dorigatti, , N. Imai,, A. Cori,, I. Dorigatti,, M., Baguelin,. C. Donnelly,, S. Riley,, N. Ferguson, (2020) Report 3: Transmissibility of 2019-n-CoV.
10 T. Liu, , J. Hu, , J. Xiao,, G. He,, M. Kang, , Z. Rong,, L. Lin,, H. Zhong,, Q. Huang,, A. Deng,, et al. (2020) Transmission dynamics of 2019 novel coronavirus (2019-nCoV). bioRxiv, doi:10.1101/2020.01.25.919787
https://doi.org/10.1101/2020.01.25.919787
11 M. Majumder, and K. D. Mandl, (2020) Early Transmissibility Assessment of a Novel Coronavirus in Wuhan, China.
[1] QB-20199-OF-CH_suppl_1 Download
[1] Pavel Pronkin, Alexander Tatikolov. Molecular docking of cyanine and squarylium dyes with SARS-CoV-2 proteases NSP3, NSP5 and NSP12[J]. Quant. Biol., 2021, 9(4): 440-450.
[2] Rudra Banerjee, Srijit Bhattacharjee, Pritish Kumar Varadwaj. A study of the COVID-19 epidemic in India using the SEIRD model[J]. Quant. Biol., 2021, 9(3): 317-328.
[3] Saroj K Biswas, Nasir U Ahmed. Mathematical modeling and optimal intervention of COVID-19 outbreak[J]. Quant. Biol., 2021, 9(1): 84-92.
[4] Cecylia S. Lupala, Xuanxuan Li, Jian Lei, Hong Chen, Jianxun Qi, Haiguang Liu, Xiao-Dong Su. Computational simulations reveal the binding dynamics between human ACE2 and the receptor binding domain of SARS-CoV-2 spike protein[J]. Quant. Biol., 2021, 9(1): 61-72.
[5] Xiaofei Yang, Tun Xu, Peng Jia, Han Xia, Li Guo, Lei Zhang, Kai Ye. Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak[J]. Quant. Biol., 2020, 8(3): 238-244.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed