Basic Reproduction Number of the 2019 Novel Coronavirus Disease in the Major Endemic Areas of China: A Latent Profile Analysis

  1. Honglv Xu
  2. Yi Zhang
  3. Min Yuan
  4. Liya Ma
  5. Meng Liu
  6. Hong Gan
  7. Wenwen Liu
  8. Gillian Gianna Anne Lum
  9. Fangbiao Tao

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Abstract

Objective: The aim of this study is to analyze the latent class of basic reproduction number ( R 0 ) trends of the 2019 novel coronavirus disease (COVID-19) in the major endemic areas of China.

Methods: The provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic areas. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R 0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19. The latent class of R 0 was analyzed using the latent profile analysis (LPA) model.

Results: The median R 0 calculated from the SARS and COVID-19 parameters were 1.84–3.18 and 1.74–2.91, respectively. The R 0 calculated from the SARS parameters was greater than that calculated from the COVID-19 parameters ( Z = −4.782 to −4.623, p < 0.01). Both R 0 can be divided into three latent classes. The initial value of R 0 in class 1 (Shandong Province, Sichuan Province, and Chongqing Municipality) was relatively low and decreased slowly. The initial value of R 0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province, and Jiangsu Province) was relatively high and decreased rapidly. Moreover, the initial R 0 value of class 3 (Hubei Province) was in the range between that of classes 1 and 2, but the higher R 0 level lasted longer and decreased slowly.

Conclusion: The results indicated that the overall R 0 trend is decreased with the strengthening of comprehensive prevention and control measures of China for COVID-19, however, there are regional differences.

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  1. Version published to 10.3389/fpubh.2021.575315
  2. ScreenIT

    SciScore for 10.1101/2020.04.13.20060228: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The present study had several strengths and limitations. The main strength of this work was to calculate the R0 based on the epidemic data of major epidemic areas in China as of February 18. However, previous research on R0 was almost prediction and estimation based on the limited epidemiological data of COVID-19 in Wuhan in the early days. Another strength was to analyze the latent class of R0 and explore the regional differences in prevention and control effects. The findings presented in this study have implications for the preliminary evaluation of the effectiveness of prevention and control measures for COVID-19 adopted in China. In addition, the research results can provide experience for other countries to response to COVID-19, and also provide support for previous prediction studies. There are several limitations to our study. Above all, the calculation of R0 is affected by multiple parameters and various factors (Delamater, et al., 2019). And yet the factors considered are relatively single in this study. Additional research is necessary to confirm the accuracy of the R0, considering the large uncertainties around estimates of R0 and the duration of infectiousness (Rebuli et al., 2018; Prem et al., 2020). Additionally, there are many methods for calculating R0 in the world (Chowell, et al., 2007), but this study did not use multiple methods to compare the results. Finally, although this study sorts out the prevention and control measures for COVID-19 in major endemic...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.04.13.20060228v1 on medRxiv