  The SIR evolutionary model predicts too sharp a decrease of the fractions of
people infected with COVID-19 in France after the start of the national
lockdown, compared to what is observed. I fit the daily hospital data: arrivals
in regular and critical care units, releases and deaths, using extended SEIR
models. These involve ratios of evolutionary timescales to branching fractions,
assumed uniform throughout a country, and the basic reproduction number, $R_0$,
before and during the national lockdown, for each region of France. The
joint-region Bayesian analysis allows precise evaluations of the time/fraction
ratios and pre-hospitalized fractions. The hospital data are well fit by the
models, except the arrivals in critical care, which decrease faster than
predicted, indicating better treatment over time. Averaged over France, the
analysis yields $R_0$= 3.4$\pm$0.1 before the lockdown and 0.65$\pm$0.04 (90%
c.l.) during the lockdown, with small regional variations. On 11 May 2020, the
Infection Fatality Rate in France was 4 $\pm$1% (90% c.l.), while the Feverish
vastly outnumber the Asymptomatic, contrary to the early phases. Without the
lockdown nor social distancing, over 2 million deaths from COVID-19 would have
occurred throughout France, while a lockdown that would have been enforced 10
days earlier would have led to less than 1000 deaths. The fraction of immunized
people reached a plateau below 1% throughout France (3% in Paris) by late April
2020 (95% c.l.), suggesting a lack of herd immunity. The widespread
availability of face masks on 11 May, when the lockdown was partially lifted,
should keep $R_0$ below unity if at least 46% of the population wear them
outside their home. Otherwise, without enhanced other social distancing, a
second wave is inevitable and cause the number of deaths to triple between
early May and October (if $R_0$=1.2) or even late June (if $R_0$=2).
