Analyzing Covid-19 Data using SIRD Models

  1. Abhijit Chakraborty
  2. Jiaying Chen
  3. Amélie Desvars-Larrive
  4. Peter Klimek
  5. Erwin Flores Tames
  6. David Garcia
  7. Leonhard Horstmeyer
  8. Michaela Kaleta
  9. Jana Lasser
  10. Jenny Reddish
  11. Beate Pinior
  12. Johannes Wachs
  13. Peter Turchin

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Abstract

The goal of this analysis is to estimate the effects of the diverse government intervention measures implemented to mitigate the spread of the Covid-19 epidemic. We use a process model based on a compartmental epidemiological framework Susceptible-Infected-Recovered-Dead (SIRD). Analysis of case data with such a mechanism-based model has advantages over purely phenomenological approaches because the parameters of the SIRD model can be calibrated using prior knowledge. This approach can be used to investigate how governmental interventions have affected the Covid-19-related transmission and mortality rate during the epidemic.

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    SciScore for 10.1101/2020.05.28.20115527: (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: 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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  2. Version published to 10.1101/2020.05.28.20115527v1 on medRxiv