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

Shi Zhao, Qianyin Lin, Jinjun Ran, Salihu S Musa, Guangpu Yang, Weiming Wang, Yijun Lou, Daozhou Gao, Lin Yang, Daihai He, Maggie H Wang, Shi Zhao, Qianyin Lin, Jinjun Ran, Salihu S Musa, Guangpu Yang, Weiming Wang, Yijun Lou, Daozhou Gao, Lin Yang, Daihai He, Maggie H Wang

Abstract

Backgrounds: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city in China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and other countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak.

Methods: Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI.

Findings: The early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0.

Conclusion: The mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and is significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

Keywords: Basic reproduction number; Novel coronavirus (2019-nCoV).

Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Figures

Figure 1
Figure 1
The scenarios of the change in the reporting rate (top panels) and the exponential growth fitting (bottom panels). The top panels, i.e., (a), (c), (e), (g), (i) and (k), show the assumed change in the reporting rate. The bottom panels, i.e., (b), (d), (f), (h), (j) and (l), show the reported (or observed, green circles), adjusted (blue dots) and fitted (blue curve) number of 2019-nCoV infections, and the blue dashed lines are the 95%CI. The vertical grey line represents the date of January 16, 2020, after which the official diagnostic protocol was released by the WHO (World Health Organization, 2020). Panels (a) and (b) show the scenarios that the reporting rate was unchanged. Panels (c) and (d) show the scenarios that the reporting rate increased by 0.5-fold. Panels (e) and (f) show the scenarios that the reporting rate increased by 1-fold. Panels (g) and (h) show the scenarios that the reporting rate increased by 2-fold. Panels (i) and (j) show the scenarios that the reporting rate increased by 4-fold. Panels (k) and (l) show the scenarios that the reporting rate increased by 8-fold.

References

    1. Assiri A., McGeer A., Perl T.M., Price C.S., Al Rabeeah A.A., Cummings D.A., Alabdullatif Z.N., Assad M., Almulhim A., Makhdoom H. Hospital outbreak of Middle East respiratory syndrome coronavirus. New Engl J Med. 2013;369(5):407–416.
    1. Bauch C.T., Lloyd-Smith J.O., Coffee M.P., Galvani A.P. Dynamically modeling SARS and other newly emerging respiratory illnesses: past, present, and future. Epidemiol (Cambridge, Mass) 2005;16(6):791–801.
    1. Bogoch I.I., Watts A., Thomas-Bachli A., Huber C., Kraemer M.U., Khan K. Pneumonia of unknown etiology in Wuhan, China: potential for international spread via commercial air travel. J Travel Med. 2020 doi: 10.1093/jtm/taaa1008.
    1. Cowling B.J., Fang V.J., Riley S., Peiris J.M., Leung G.M. Estimation of the serial interval of influenza. Epidemiology (Cambridge, Mass) 2009;20(3):344.
    1. de Silva U., Warachit J., Waicharoen S., Chittaganpitch M. A preliminary analysis of the epidemiology of influenza A (H1N1) v virus infection in Thailand from early outbreak data, June-July 2009. Eurosurveillance. 2009;14(31):19292.
    1. Donnelly C.A., Ghani A.C., Leung G.M., Hedley A.J., Fraser C., Riley S. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet (London, England) 2003;361(9371):1761–1766.
    1. Hubei provincial government . Hubei provincial government; 2020. Strengthening the new coronavirus infection of pneumonia prevention and control.
    1. Imai N., Dorigatti I., Cori A., Riley S., Ferguson N.M. Preprint published by the Imperial College London; China: 2020. Estimating the potential total number of novel Coronavirus (2019-nCoV) cases in Wuhan City.
    1. Leung K., Wu J.T., Leung G.M. Preprint published by the School of Public Health of the University of Hong Kong; 2020. Nowcasting and forecasting the Wuhan 2019-nCoV outbreak.
    1. Lin Q., Chiu A.P., Zhao S., He D. Modeling the spread of Middle East respiratory syndrome coronavirus in Saudi Arabia. Stat Methods Med Res. 2018;27(7):1968–1978.
    1. Lipsitch M., Cohen T., Cooper B., Robins J.M., Ma S., James L. Transmission dynamics and control of severe acute respiratory syndrome. Science. 2003;300(5627):1966–1970.
    1. National Health Commission of the People’s Republic of China . 2020. ‘Definition of suspected cases of unexplained pneumonia’, the National Health Commission of the People’s Republic of China (in Chinese)
    1. Read J.M., Bridgen J.R., Cummings D.A., Ho A., Jewell C.P. Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. medRxiv. 2020 2020.2001.2023.20018549.
    1. Riou J., Althaus C.L. Pattern of early human-to-human transmission of Wuhan 2019-nCoV. bioRxiv. 2020 2020.2001.2023.917351.
    1. Shen M., Peng Z., Xiao Y., Zhang L. Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China. bioRxiv. 2020;2020 2001.2023.916726.
    1. Wallinga J., Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc R Soc B: Biolo Sci. 2007;274(1609):599–604.
    1. Wallinga J., Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol. 2004;160(6):509–516.
    1. World Health Organization . 2020. ‘Pneumonia of unknown cause – China’, Emergencies preparedness, response, Disease outbreak news, World Health Organization (WHO)
    1. World Health Organization . 2020. Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases, World Health Organization (WHO)
    1. Wu J.T., Cowling B.J., Lau E.H., Ip D.K., Ho L.-M., Tsang T. School closure and mitigation of pandemic (H1N1) 2009, Hong Kong. Emerg Infect Dis. 2010;16(3):538.
    1. Wuhan Municipal Health Commission, China . 2020. ‘News press and situation reports of the pneumonia caused by novel coronavirus’, from December 31, 2019 to January 21, 2020 released by the Wuhan municipal health commission, China.
    1. Zhao S., Musa S.S., Fu H., He D., Qin J. Simple framework for real-time forecast in a data-limited situation: the Zika virus (ZIKV) outbreaks in Brazil from 2015 to 2016 as an example. Parasites Vectors. 2019;12(1):344.

Source: PubMed

3
Tilaa