Fit of French COVID-19 hospital data with different evolutionary models: regional measures of $R_0$ before and during lockdown
Mamon Ga
Abstract
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 time to better treat COVID-19 patients without resorting to critical care. The basic reproductive factor, averaged over France, was $R_0$=3.4$\pm$0.1 before the lockdown and 0.65$\pm$0.04 (90% c.l.) during it, both with small regional variations. On 11 May 2020, the Infection Fatality Rate in France is 4$\pm$1% (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.