A data-driven epidemiological model to explain the Covid-19 pandemic in multiple countries and help in choosing mitigation strategies

  1. M. J. Beira
  2. A. Kumar
  3. L. Perfeito
  4. J. Gonçalves-Sá
  5. P. J. Sebastião

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Abstract

Accurate models are fundamental to understand the dynamics of the COVID-19 pandemic and to evaluate different mitigation strategies. Here, we present a multi-compartmental model that fits the epidemiological data for eleven countries, despite the reduced number of fitting parameters. This model consistently explains the data for the daily infected, recovered, and dead over the first six months of the pandemic. The good quality of the fits makes it possible to explore different scenarios and evaluate the impact of both individual and collective behaviors and government-level decisions to mitigate the epidemic. We identify robust alternatives to lockdown, such as self-protection measures, and massive testing. Furthermore, communication and risk perception are fundamental to modulate the success of different strategies. The fitting/simulation tool is publicly available for use and test of other models, allowing for comparisons between different underlying assumptions, mitigation measures, and policy recommendations.

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    SciScore for 10.1101/2020.08.15.20175588: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 3, 13 and 14. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  2. Version published to 10.1101/2020.08.15.20175588v1 on medRxiv