A logistic model and predictions for the spread of the COVID-19 pandemic

  1. Baolian Cheng
  2. Yi-Ming Wang

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Abstract

The rapid spread of COVID-19 worldwide presents a great challenge to epidemic modelers. Model outcomes vary widely depending on the characteristics of a pathogen and the models. Here, we present a logistic model for the epidemic spread and divide the spread of the novel coronavirus into two phases: the first phase is a natural exponential growth phase that occurs in the absence of intervention and the second phase is a regulated growth phase that is affected by enforcing social distancing and isolation. We apply the model to a number of pandemic centers. Our results are in good agreement with the data to date and show that social distancing significantly reduces the epidemic spread and flattens the curve. Predictions on the spreading trajectory including the total infections and peak time of new infections for a community of any size are made weeks ahead, providing the vital information and lead time needed to prepare for and mitigate the epidemic. The methodology presented here has immediate and far-reaching applications for ongoing outbreaks or similar future outbreaks of other emergent infectious diseases.

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  1. Version published to 10.1063/5.0028236
  2. ScreenIT

    SciScore for 10.1101/2020.04.27.20081281: (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

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  3. Version published to 10.1101/2020.04.27.20081281v1 on medRxiv