Forecasting the Wuhan coronavirus (2019-nCoV) epidemics using a simple (simplistic) model

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Confirmed infection cases in mainland China were analyzed using the data up to January 28, 2020 (first 13 days of reliable confirmed cases). In addition, all available data up to February 3 were processed the same way. For the first period the cumulative number of cases followed an exponential function. However, from January 28, we discerned a downward deviation from the exponential growth. This slower-than-exponential growth was also confirmed by a steady decline of the effective reproduction number. A backtrend analysis suggested the original basic reproduction number R 0 to be about 2.4 – 2.5. We used a simple logistic growth model that fitted very well with all data reported until the time of writing. Using this model and the first set of data, we estimate that the maximum cases will be about 21,000 reaching this level in mid-February. Using all available data the maximum number of cases is somewhat higher at 29,000 but its dynamics does not change. These predictions do not account for any possible other secondary sources of infection.

Article activity feed

  1. SciScore for 10.1101/2020.02.04.20020461: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.