Dynamic Estimation of Epidemiological Parameters of Covid-19 Outbreak and Effects of Interventions on Its Spread

  1. Hongzhe Zhang
  2. Xiaohang Zhao
  3. Kexin Yin
  4. Yiren Yan
  5. Wei Qian
  6. Bintong Chen
  7. Xiao Fang

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Abstract

A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.

Methods

We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.

Results

The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.

Conclusions

This case study by our proposed method affirms the believed importance and effectiveness of imposing tight nonessential travel restrictions and affirm the importance and effectiveness of government interventions ( e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread.

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  1. Version published to 10.4081/jphr.2021.1906
  2. ScreenIT

    SciScore for 10.1101/2020.04.01.20050310: (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:
    Although our study uncovers some convincing approximation on the dynamic progression patterns of COVID-19 in Wuhan, there remain some limitations. Here we assume that all recovered patients become totally immune to the novel coronavirus infection. If recovered patients are still susceptible, an extension from SIQR to SIQRS (that is, Susceptible-Infective-Quarantined-Removed-Susceptible) may be employed, while the general framework of our method remains largely applicable. In addition, the removed compartment in our model contains both death and cured cases, which prevents us from estimating the time-varying case fatality rates. Consequently, our assessment of large scale hospitalization does not reflect its effectiveness in death toll reduction, although literature has shown that promptly hospitalizing infected people could reduce the fatality rate for older adults and even for those with mild symptoms20,21,22,23,24. Future studies may investigate the trajectory of fatality rates by treating death and cured cases separately. In summary, our finding provides a quantitative illustration that the scale of infection size in Wuhan can be multi-fold higher than officially reported numbers and partially explains the excessive stress experienced by frontline medical workers despite seemingly modest case number increases reported during late January of 2020. This work thus gives a cautionary tale for drawing immediate public health conclusions solely based on unadjusted official case ...

    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 10.1101/2020.04.01.20050310v2 on medRxiv
  4. Version published to 10.1101/2020.04.01.20050310v1 on medRxiv