Chinese Science Bulletin, Volume 65 , Issue 22 : 2348-2355(2020) https://doi.org/10.1360/TB-2020-0143

A predictive model for COVID-19 spreading

More info
  • ReceivedFeb 18, 2020
  • AcceptedApr 7, 2020
  • PublishedApr 8, 2020


As the novel coronavirus disease 2019 (COVID-19) has become a Public Health Emergency of International Concern, it is greatly significant to accurately predict the disease’s incoming trend. Herein, we performed a stage-rolling Susceptible-Exposed-Infectious-Recovered (SEIR) model to measure the evolution of the basic reproduction number of COVID-19, based on the number of confirmed infections announced by the National Health Commission of the People’s Republic of China.

We assumed that the infected number under the spreading of infectious diseases will generally experience two different stages. In the first stage, due to the public’s ignorance of the severity and harmfulness of the disease, the infected population grows exponentially and the process of disease transmission can be considered as the classic SEIR model with a constant basic reproduction number. Consequently, with limited awareness of the epidemic situation, there is a lack of effective preventive measures to control disease transmission.

In the second stage, various control measures and medical resources are introduced in succession by the government, as well as the public gradually takes effective preventions (e.g. keep social distance and wear masks) based on the knowledge of the disease transmission. Collectively, the infected population grows much slower than the first stage. We performed a stage-rolling SEIR model, in which the basic reproduction number changes every day. Based on this model, the number of daily basic reproduction is estimated from the daily new infection number. We found that the daily basic reproduction number is expected to decline continually until it is less than 1, which means the eradication of the disease.

Leveraging the evolution of the basic reproduction number, we extrapolate the incoming daily basic reproduction number, based on which we further predict the incoming trend of COVID-19 spreading in terms of the daily infection number. Our predictive model estimates that at the end of the epidemic, the total number of infections in China is nearly 14000 except for Hubei Province, and 32000 except for Wuhan city. We also found that in most parts of China, the number of newly confirmed infections increases linearly rather than exponentially before the day of “Wuhan travel restrictions”, implying that the prevention and containing the infected people from Wuhan at the eve of the Spring Festival has been effective from the beginning.

Funded by



[1] Lekone P E, Finkenstädt B F. Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study. Biometrics, 2006, 62: 1170-1177 CrossRef Google Scholar

[2] Xie J R, Meng F H, Huang Y W, et al. Optimal devoted resource strategies to epidemic extinction by increasing recovery rate. Intl J Mod Phys C, 2019, 31: 1–10. Google Scholar

[3] Newman M E J. Spread of epidemic disease on networks. Phys Rev E, 2002, 66: 016128. Google Scholar

[4] Chowell G, Nishiura H. Transmission dynamics and control of Ebola virus disease (EVD): A review. BMC Med, 2014, 12: 196. Google Scholar

[5] Garnett G P. The basic reproductive rate of infection and the course of HIV epidemics. AIDS Pat Care STDs, 1998, 12: 435–449. Google Scholar

[6] Chen X L, Zhou T S, Feng L, et al. Nontrivial resource requirement in the early stage for containment of epidemics. Phys Rev E, 2019, 100: 032310. Google Scholar

[7] Dye C, Gay N. Modeling the SARS epidemic. Science, 2003, 300: 1884–1885. Google Scholar

[8] Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393: 440. Google Scholar

[9] Barabási A L, Albert R. Emergence of scaling in random networks. Science, 1999, 286: 509–512. Google Scholar

[10] Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks. Phys Rev Lett, 2001, 86: 3200. Google Scholar

[11] Li Q, Guan X H, Wu P, et al. Early transmission dynamics in wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med, 2020, 382: 1199–1207. Google Scholar

  • Figure 1

    (Color online) Exponential growth of the number of COVID-19 infections. (a) and (b) Baidu index and Google index of some related keyworks, respectively; (c) predicted and ture accumulated number of confirmed infections in China. Similar curves in China expect Hubei Province and China expect Wuhan city are plotted in (d) and (e), respectively

  • Figure 2

    (Color online) Value of R˜0(t) estimated by the stage-rolling SEIR model. (a–c) R˜0(t) of the whole country, the whole country except Hubei Province, the whole country except Wuhan city, respectively. (d–f) Plot the number of newly confirmed cases per day in the whole country, the whole country except Hubei Province and the whole country except Wuhan city, respectively

  • Figure 3

    (Color online) True and predicted values of R˜0(t) in the country except Hubei Province. The solid circle symbols denote the ture value and the hollow diamond symbols denote the predicted one

  • Figure 4

    (Color online) Predicted numer of COVID-19 infections. The circles are the true values while other symbols are the predicted values. (a) and (b) Predicted values in the whole country except Hubei Province; (c) and (d) predicted values in the whole country except Wuhan city; (e) and (f) predicted values in the Hubei Province except Wuhan city

  • Figure 5

    (Color online) Basic reproduction number R˜0(t) and fitted slope for the number of newly COVID-19 infections (in logarithmic coordinate). (a–c) Present the number of cases in the whole country except Hubei Province, the whole country except Wuhan city and the Hubei Province except Wuhan city, respectively. The relationship between R˜0(t) and fitted slope is described in Eqs. (11) and (12)

  • Table 1   Average basic reproduction number measured by various T in the country except Hubei Province and except Wuhan city between Feb. 7–11













Copyright 2020  CHINA SCIENCE PUBLISHING & MEDIA LTD.  中国科技出版传媒股份有限公司  版权所有

京ICP备14028887号-23       京公网安备11010102003388号