Evaluating the different control policies for COVID-19 between mainland China and European countries by a mathematical model in the confirmed cases

  1. Feng Lin
  2. Yi Huang
  3. Huifang Zhang
  4. Xu He
  5. Yonghua Yin
  6. Jiaxin Liu

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

This study focuses on evaluating the different policies of controlling the outbreak of COVID-19 in mainland China and in some European countries. The study is based on mathematical model which is a modified susceptible-infected-recovered (SIR) model. The model takes death and recovery into consideration which in convenience is called the susceptible-infected-recovered-death (SIRD) model. The criterion for the recovered patients is assumed by COVID-19 nucleic acid testing negative. The mathematical model is constructed by retrospective study. Determination of the parameters in the model is based on the epidemic bulletin supplied by the Chinese Center for Disease Control and Prevention (CDC) and National Health Commission of the People’s Republic of China (NHC) from Jan 16 2020 to Mar 5 2020. The data cover the date when the epidemic situation is reported and the data showed that the epidemic situation is almost under control in China. The mathematical model mainly simulates the active cases and the deaths during the outbreak of COVID-19. Then apply the mathematical model to simulate the epidemic situations in Italy and Spain, which are suffering the outbreak of COVID-19 in Europe. The determination of the parameters for the 2 European countries is based on the data supplied by Worldometers. By comparing the difference of the parameters based on the same mathematical model, it is possible to evaluate the different policies in different countries. It turns out that the relatively easing control policies might lead to rapid spread of the disease.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.17.20068775: (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.

  2. Version published to 10.1101/2020.04.17.20068775v1 on medRxiv