Estimation of the epidemic properties of the 2019 novel coronavirus: A mathematical modeling study

  1. Jinghua Li
  2. Yijing Wang
  3. Stuart Gilmour
  4. Mengying Wang
  5. Daisuke Yoneoka
  6. Ying Wang
  7. Xinyi You
  8. Jing Gu
  9. Chun Hao
  10. Liping Peng
  11. Zhicheng Du
  12. Dong Roman Xu
  13. Yuantao Hao

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

The 2019 novel Coronavirus (COVID-19) emerged in Wuhan, China in December 2019 and has been spreading rapidly in China. Decisions about its pandemic threat and the appropriate level of public health response depend heavily on estimates of its basic reproduction number and assessments of interventions conducted in the early stages of the epidemic.

Methods

We conducted a mathematical modeling study using five independent methods to assess the basic reproduction number (R0) of COVID-19, using data on confirmed cases obtained from the China National Health Commission for the period 10 th January – 8 th February. We analyzed the data for the period before the closure of Wuhan city (10 th January – 23 rd January) and the post-closure period (23 rd January – 8 th February) and for the whole period, to assess both the epidemic risk of the virus and the effectiveness of the closure of Wuhan city on spread of COVID-19.

Findings

Before the closure of Wuhan city the basic reproduction number of COVID-19 was 4.38 (95% CI: 3.63 – 5.13), dropping to 3.41 (95% CI: 3.16 – 3.65) after the closure of Wuhan city. Over the entire epidemic period COVID-19 had a basic reproduction number of 3.39 (95% CI: 3.09 – 3.70), indicating it has a very high transmissibility.

Interpretation

COVID-19 is a highly transmissible virus with a very high risk of epidemic outbreak once it emerges in metropolitan areas. The closure of Wuhan city was effective in reducing the severity of the epidemic, but even after closure of the city and the subsequent expansion of that closure to other parts of Hubei the virus remained extremely infectious. Emergency planners in other cities should consider this high infectiousness when considering responses to this virus.

Funding

National Natural Science Foundation of China, China Medical Board, National Science and Technology Major Project of China

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.02.18.20024315: (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:
    Our model avoids the limitations of specific modeling choices by combining several methods with a Poisson Loss weight, using the most current and accurate case diagnosis. Through this approach we calculate a more robust estimate than previous studies, and find a higher value of R0. Ours is also the first study to compare the pre- and post-closure periods in the data, and thus the first study to make a judgment about the effectiveness of this strategy. Given the high risk of epidemic from COVID-19, it is important to assess the value of this strategy before the disease takes hold in another global city. This study has several limitations. It was based on confirmed cases, and by excluding suspected cases or mild cases may have under-estimated the rate of spread of the disease. We did not estimate the values of the parameters defining the transition rate from exposed to infectious, or infected to recovered, but fixed them at previously published values. This was a necessary decision because the clinical features of the disease are not yet fully understood, and may affect estimates. However, our intuition after fitting these models is that the maximum likelihood estimate of the force of infection naturally adjusts to fit the value of the recovery rate, and produces a broadly similar value of the basic reproduction number as a result. Furthermore, to adjust for the still-arbitrary nature of these estimates of key parameters, we used some methods that do not depend on any assumptio...

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.02.18.20024315v1 on medRxiv