Autocatalytic Model for Covid-19 Progression in a Country

  1. Anatoly Chernyshev

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Abstract

Herewith we present a computational model for the forecasting of cumulative diagnosed cases of Covid-19 pneumonia within a country. The only explicit parameter of the model is the population density. The implicit parameter is a moving average ambient temperature, currently integrated into the kinetic constants. Other finer details pertaining to the mechanism of the pathogen SARS-CoV-2 spread within a given region are implicitly manifested in the exponent parameters derived from the non-linear fitting of the published data on Covid-19 occurrence. The performance of the model is demonstrated on a few selected countries, and on the Diamond Princess cruising ship outbreak. The model might be used as an aiding tool for the policy makers regarding the decisions on the containment measures and quarantine regime required.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.03.20052985: (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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  2. Version published to 10.1101/2020.04.03.20052985 on medRxiv