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 introduce three extensions to these models to fit the daily arrivals in French hospitals, as well in critical care , releases and deaths . These models 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  hospital data are well fit by the models with the exception of the arrivals in critical care, which are found to decrease in time faster than predicted by all models. This suggests that hospitals have learnt over timeto better treat COVID-19 patients without resorting to critical care. The basic reproductive factor, averaged over France, was R_0= 3.4\pm0.1 before the lockdown and 0.65\pm0.04 (90\% c.l.) during it, both with small regional variations. On 11 May 2020, the Infection Fatality Rate in France is 4 \pm1\% (90\% c.l.) and constant , 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  . 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 and that a second wave of the pandemic is possible during the partial lifting of the national lockdown. After the partial lifting of the lockdown, if R_0 is as high as 1.5, then a second wave will lead to 60 thousand deaths by mid-July and over a million by October , while if R_0 is 1.2 or lower, the pandemic is delayed with deaths rising as late as August, allowing for timely governmental response .
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\pm0.1 before the lockdown and 0.65\pm0.04 (90\% c.l.) during the lockdown, with small regional variations. On 11 May 2020, the Infection Fatality Rate in France was 4 \pm1\% (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) .
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