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

Romain Gauchon 1 Nicolas Ponthus 2 Catherine Pothier 3 Christophe Rigotti 4, 5, 6 Vitaly Volpert 7 Stéphane Derrode 3 Jean-Pierre Bertoglio 8 Alexis Bienvenüe 1 Pierre-Olivier Goffard 1 Anne Eyraud-Loisel 1 Simon Pageaud 9 Jean Iwaz 9 Stéphane Loisel 1 Pascal Roy 9, 10 
3 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
4 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
5 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
7 DRACULA - Multi-scale modelling of cell dynamics : application to hematopoiesis
CGPhiMC - Centre de génétique et de physiologie moléculaire et cellulaire, Inria Grenoble - Rhône-Alpes, ICJ - Institut Camille Jordan [Villeurbanne]
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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https://hal.archives-ouvertes.fr/hal-03341704
Contributor : Christophe Rigotti Connect in order to contact the contributor
Submitted on : Sunday, September 12, 2021 - 12:39:50 AM
Last modification on : Wednesday, November 9, 2022 - 3:47:30 AM
Long-term archiving on: : Monday, December 13, 2021 - 6:01:32 PM

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Romain Gauchon, Nicolas Ponthus, Catherine Pothier, Christophe Rigotti, Vitaly Volpert, et al.. Lessons learnt from the use of compartmental models over the COVID-19 induced lockdown in France. [Research Report] Université Lyon 1; Ecole Centrale de Lyon; INSA Lyon. 2021, p. 39. ⟨hal-03341704⟩

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