Lessons learnt from the use of compartmental models over the COVID-19 induced lockdown in France

  1. Romain Gauchon
  2. Nicolas Ponthus
  3. Catherine Pothier
  4. Christophe Rigotti
  5. Vitaly Volpert
  6. Stéphane Derrode
  7. Jean-Pierre Bertoglio
  8. Alexis Bienvenüe
  9. Pierre-Olivier Goffard
  10. Anne Eyraud-Loisel
  11. Simon Pageaud
  12. Jean Iwaz
  13. Stéphane Loisel
  14. Pascal Roy

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Abstract

Background

Compartmental models help making public health decisions. They were used during the COVID-19 outbreak to estimate the reproduction numbers and predict the number of hospital beds required. This study examined the ability of closely related compartmental models to reflect equivalent epidemic dynamics.

Methods

The study considered three independently designed compartmental models that described the COVID-19 outbreak in France. Model compartments and parameters were expressed in a common framework and models were calibrated using the same hospitalization data from two official public databases. The calibration procedure was repeated over three different periods to compare model abilities to: i) fit over the whole lockdown; ii) predict the course of the epidemic during the lockdown; and, iii) provide profiles to predict hospitalization prevalence after lockdown. The study considered national and regional coverages.

Results

The three models were all flexible enough to match real hospitalization data during the lockdown, but the numbers of cases in the other compartments differed. The three models failed to predict reliably the number of hospitalizations after the fitting periods at national as at regional scales. At the national scale, an improved calibration led to epidemic course profiles that reflected hospitalization dynamics and reproduction numbers that were coherent with official and literature estimates.

Conclusion

This study shows that prevalence data are needed to further refine the calibration and make a selection between still divergent models. This underlines strongly the need for repeated prevalence studies on representative population samples.

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  1. Version published to 10.1101/2021.01.11.21249565v3 on medRxiv
  2. Version published to 10.1101/2021.01.11.21249565v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.11.21249565: (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
    With SEIRAHm, a constrained Nelder-Mead method was applied using constrOptim function in R. With EHESPm and INSERMm, a first constrained Nelder-Mead fit was applied using package lmfit in Python.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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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  4. Version published to 10.1101/2021.01.11.21249565v1 on medRxiv