Epidemic Models for COVID-19 during the First Wave from February to May 2020: a Methodological Review

  1. Marie Garin
  2. Myrto Limnios
  3. Alice Nicolaï
  4. Ioannis Bargiotas
  5. Olivier Boulant
  6. Stephen E. Chick
  7. Amir Dib
  8. Theodoros Evgeniou
  9. Mathilde Fekom
  10. Argyris Kalogeratos
  11. Christophe Labourdette
  12. Anton Ovchinnikov
  13. Raphaël Porcher
  14. Camille Pouchol
  15. Nicolas Vayatis

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Abstract

We review epidemiological models for the propagation of the COVID-19 pandemic during the early months of the outbreak: from February to May 2020. The aim is to propose a methodological review that highlights the following characteristics: ( i ) the epidemic propagation models, ( ii ) the modeling of intervention strategies, ( iii ) the models and estimation procedures of the epidemic parameters and ( iv ) the characteristics of the data used. We finally selected 80 articles from open access databases based on criteria such as the theoretical background, the reproducibility, the incorporation of interventions strategies, etc . It mainly resulted to phenomenological, compartmental and individual-level models. A digital companion including an online sheet, a Kibana interface and a markdown document is proposed. Finally, this work provides an opportunity to witness how the scientific community reacted to this unique situation.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.02.21263033: (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

    Software and Algorithms
    SentencesResources
    First, to establish the main corpus of the review, the process was based on an extended search based on a set of keywords applied on the three main online open access archives: arXiv, biorXiv, medrXiv.
    arXiv
    suggested: (arXiv, RRID:SCR_006500)
    biorXiv
    suggested: (bioRxiv, RRID:SCR_003933)

    Results from OddPub: Thank you for sharing your code.

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.09.02.21263033v1 on medRxiv