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« Analysis of French COVID-19 hospital data with a SEAFHCDRO model »

Gary Mamon
Institut d'Astrophysique de Paris (Paris, France)

The French daily hospital data with general admissions, admissions to critical care, deaths and releases, allows to objectively fit models of the growth of the COVID-19 pandemic in France. After an introduction to simple SIR evolutionary models, I will show that the data invalidates these models. I will present 7-, 8- and 9-phase models, which appear to be the simplest required to model the hospital data. These models involve up to 9 ratios of timescales over branching fractions, as well as the R_0 factors before and after the national lockdown and a normalization. I will show the results of Bayesian modeling for France considered as a single region, as well as considering 8 French départements, which are assumed to be genetically equivalent (hence same ratios) but with their own R_0 factors and normalizations. These results show that the national lockdown was a success, suggesting that well over one million deaths would have occurred in France without it. I will also show predictions for the fraction of the population that will have become immunized to the current strain of SARS-2, as well as for future evolution past the partial lifting of the lockdown this May 11.

mardi 5 mai 2020 - 10:30
Webinaire, Institut d'Astrophysique
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