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

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


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[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