Modeling COVID‐19: Forecasting and analyzing the dynamics of the outbreaks in Hubei and Turkey

  1. Ibrahim Halil Aslan
  2. Mahir Demir
  3. Michael Morgan Wise
  4. Suzanne Lenhart

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Abstract

As the pandemic of Coronavirus Disease 2019 (COVID‐19) rages worldwide, accurate modeling of the dynamics thereof is essential. However, since the availability and quality of data vary dramatically from region to region, accurate modeling directly from a global perspective is difficult. Nevertheless, via local data collected by certain regions, it is possible to develop accurate local prediction tools, which may be coupled to develop global models. In this study, we analyze the dynamics of local outbreaks of COVID‐19 via a system of ordinary differential equations (ODEs). Utilizing a large amount of data available from the ebbing outbreak in Hubei, China, as a testbed, we predict the trajectory of daily cases, daily deaths, and other features of the Hubei outbreak. Through numerical experiments, we observe the effects of social distancing on the dynamics of the outbreak. Using knowledge gleaned from the Hubei outbreak, we apply our model to analyze the dynamics of the outbreak in Turkey. We provide forecasts for the peak of the outbreak and the daily number of cases and deaths in Turkey, by varying levels of social distancing and the transition rate which is from infected class to confirmed infected class.

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  1. Version published to 10.1002/mma.8181
  2. ScreenIT

    SciScore for 10.1101/2020.04.11.20061952: (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.

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  3. Version published to 10.1101/2020.04.11.20061952v1 on medRxiv