Preliminary prediction of the basic reproduction number of the novel coronavirus 2019-nCoV_Chinese Journal of Evidence-Based Medicine

Authors：

ZHOU Tao ¹ ,  LIU Quanhui ² , YANG Zimo ³ , LIAO Jingyi ⁴ , YANG Kexin ² , BAI Wei ⁵ ,  LU Xin ⁶ ,  ZHANG Wei ⁷

1. Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, P.R.China;
2. College of Computer Science, Sichuan University, Chengdu, 610065, P.R.China;
3. Beijing AiQiYi Science & Technology Co. Ltd., Beijing, 100080, P.R.China;
4. Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, P.R.China;
5. CompleX Lab, University of Electronic Science and Technology of China, Chengdu, 611731, P.R.China;
6. College of Systems Engineering, National University of Defense Technology, Changsha, 410073, P.R.China;
7. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610047, P.R.China;

Corresponding author：

LIU Quanhui, Email: quanhuiliu@scu.edu.cn; LU Xin, Email: xin.lu@flowminder.org; ZHANG Wei, Email: zhangwei@wchscu.cn

Keywords：

2019 novel coronavirus (2019-nCoV); Basic reproduction number; Epidemiology

DOI：

10.7507/1672-2531.202001118

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objectives To estimate the basic reproduction number of the novel coronavirus (2019-nCoV) and to provide support to epidemic preparedness and response.Methods Based on the susceptible–exposed–infected–removed (SEIR) compartment model and the assumption that the infection cases with symptoms occurred before January 26, 2020 were resulted from free propagation without intervention, we estimated the basic reproduction number of 2019-nCoV according to the reported confirmed cases and suspected cases, as well as theoretical estimated number of infected cases by other research teams, together with some epidemiological determinants learned from the severe acute respiratory syndrome.Results The basic reproduction number fall between 2.8 to 3.3 by using the real-time reports on the number of 2019-nCoV infected cases from People’s Daily in China, and fall between 3.2 and 3.9 on the basis of the predicted number of infected cases from international colleagues.Conclusions The early transmission capability of 2019-nCoV is close to or slightly higher than SARS. It is a controllable disease with moderate-high transmissibility. Timely and effective control measures are capable to quickly reduce further transmission.

Citation： ZHOU Tao, LIU Quanhui, YANG Zimo, LIAO Jingyi, YANG Kexin, BAI Wei, LU Xin, ZHANG Wei. Preliminary prediction of the basic reproduction number of the novel coronavirus 2019-nCoV. Chinese Journal of Evidence-Based Medicine, 2020, 20(3): 359-364. doi: 10.7507/1672-2531.202001118 Copy

1.	Adnerson RM, May RM, Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford, 1991.
2.	Available from: https://3g.dxy.cn/newh5/view/pneumonia?scene=2&clicktime=1579583352&enterid=1579583352&from=timeline&isappinstalled=0.
3.	Available from: https://m.weibo.cn/u/2803301701.
4.	Chinazzi M, Davis JT, Gioannini C, et al. Series reports entitled “preliminary assessment of the international spreading risk associated with the 2019 novel coronavirus (2019-nCoV) outbreak in Wuhan City” (unpublished).
5.	Donnelly CA, Ghani AC, Leung GM, et al. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet, 361, 2003: 1761-1766.
6.	Chan JFW, Yuan S, Kok KH, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet (in press).
7.	Pastor-Satorras R, Castellano C, Mieghem PV, et al. Epidemic processes in complex networks. Review of Modern Physis, 2015, 87(3): 925-979.
8.	Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proceedings of the Royal Society B: Biological Sciences, 2007, 274(1609): 599-604.
9.	Imai N, Dorigatti I, Cori A, et al. Series Reports Entitled “Estimating the potential total number of novel Coronavirus in Wuhan City, China” (unpublished).
10.	Read JM, Bridgen JRE, Cummings DAT, et al. Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. Preprint in MedRXiv 2020 (unpublished).
11.	Leo YS, Chen M, Heng BH, et al. Severe acute respiratory syndrome-Singapore 2003. Morb Mortal Weekly Report, 2003, 52(18): 405-432.
12.	Lipsitch M, Cohen T, Cooper B, et al. Transmission dynamics and control of severe acute respiratory syndrome. Science, 2003, 300(5627): 1966-1970.
13.	Imai N, Cori A, Dorigatti I, et al. Transmissibility of 2019-nCoV (unpublished).
14.	Riley S. Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science, 2003, 300(5627): 1961-1966.
15.	Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol, 2004, 160(6): 509-517.
16.	Lessler J, Chaisson LH, Kucirka LM, et al. Assessing the global threat from Zika virus. Science, 2016, 353: aaf8160.
17.	Majumder MS, Rivers C, Lofgren E, et al. Estimation of MERS-coronavirus reproductive number and case fatality rate for the spring 2014 Saudi Arabia outbreak: insights from publicly available data. PLoS Currents Outbreaks, 2014, 6.
18.	Eichner M, Dietz K. Transmission potential of smallpox: estimates based on detailed data from an outbreak. Am J Epidemiol, 2003, 158(2): 110-117.
19.	Balcan D, Gonçalves B, Hu H, et al. Modeling the spatial spread of infectious diseases: The Global Epidemic and Mobility computational model. J Com Sci, 2010, 1(3): 132-145.
20.	Brockmann D, Helbing D. The hidden geometry of complex, network-driven contagion phenomena. Science, 2013, 342(6164): 1337-1342.
21.	Bengtsson L, Gaudart J, Lu X, et al. Using mobile phone data to predict the spatial spread of cholera. Sci Rep, 2015, 5: 8923.
22.	Liu QH, Ajelli M, Aleta A, et al. Measurability of the epidemic reproduction number in data-driven contact networks. PNAS, 2018, 115(50): 12680-12685.

1. Adnerson RM, May RM, Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford, 1991.
2. Available from: https://3g.dxy.cn/newh5/view/pneumonia?scene=2&clicktime=1579583352&enterid=1579583352&from=timeline&isappinstalled=0.
3. Available from: https://m.weibo.cn/u/2803301701.
4. Chinazzi M, Davis JT, Gioannini C, et al. Series reports entitled “preliminary assessment of the international spreading risk associated with the 2019 novel coronavirus (2019-nCoV) outbreak in Wuhan City” (unpublished).
5. Donnelly CA, Ghani AC, Leung GM, et al. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet, 361, 2003: 1761-1766.
6. Chan JFW, Yuan S, Kok KH, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet (in press).
7. Pastor-Satorras R, Castellano C, Mieghem PV, et al. Epidemic processes in complex networks. Review of Modern Physis, 2015, 87(3): 925-979.
8. Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proceedings of the Royal Society B: Biological Sciences, 2007, 274(1609): 599-604.
9. Imai N, Dorigatti I, Cori A, et al. Series Reports Entitled “Estimating the potential total number of novel Coronavirus in Wuhan City, China” (unpublished).
10. Read JM, Bridgen JRE, Cummings DAT, et al. Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. Preprint in MedRXiv 2020 (unpublished).
11. Leo YS, Chen M, Heng BH, et al. Severe acute respiratory syndrome-Singapore 2003. Morb Mortal Weekly Report, 2003, 52(18): 405-432.
12. Lipsitch M, Cohen T, Cooper B, et al. Transmission dynamics and control of severe acute respiratory syndrome. Science, 2003, 300(5627): 1966-1970.
13. Imai N, Cori A, Dorigatti I, et al. Transmissibility of 2019-nCoV (unpublished).
14. Riley S. Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science, 2003, 300(5627): 1961-1966.
15. Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol, 2004, 160(6): 509-517.
16. Lessler J, Chaisson LH, Kucirka LM, et al. Assessing the global threat from Zika virus. Science, 2016, 353: aaf8160.
17. Majumder MS, Rivers C, Lofgren E, et al. Estimation of MERS-coronavirus reproductive number and case fatality rate for the spring 2014 Saudi Arabia outbreak: insights from publicly available data. PLoS Currents Outbreaks, 2014, 6.
18. Eichner M, Dietz K. Transmission potential of smallpox: estimates based on detailed data from an outbreak. Am J Epidemiol, 2003, 158(2): 110-117.
19. Balcan D, Gonçalves B, Hu H, et al. Modeling the spatial spread of infectious diseases: The Global Epidemic and Mobility computational model. J Com Sci, 2010, 1(3): 132-145.
20. Brockmann D, Helbing D. The hidden geometry of complex, network-driven contagion phenomena. Science, 2013, 342(6164): 1337-1342.
21. Bengtsson L, Gaudart J, Lu X, et al. Using mobile phone data to predict the spatial spread of cholera. Sci Rep, 2015, 5: 8923.
22. Liu QH, Ajelli M, Aleta A, et al. Measurability of the epidemic reproduction number in data-driven contact networks. PNAS, 2018, 115(50): 12680-12685.

Chinese Journal of Evidence-Based Medicine

Preliminary prediction of the basic reproduction number of the novel coronavirus 2019-nCoV

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