  We discovered that the time evolution of the inverse fractional daily growth
of new infections, N/dN, in the current outbreak of COVID-19 is accurately
described by a universal function, namely the two-parameter Gumbel cumulative
function, in all countries that we have investigated. While the two Gumbel
parameters, as determined bit fits to the data, vary from country to country
(and even within different regions of the same country), reflecting the
diversity and efficacy of the adopted containment measures, the functional form
of the evolution of N/dN appears to be universal. The result of the fit in a
given region or country appears to be stable against variations of the selected
time interval. This makes it possible to robustly estimate the two parameters
from the data data even over relatively small time periods. In turn, this
allows one to predict with large advance and well-controlled confidence levels,
the time of the peak in the daily new infections, its magnitude and duration
(hence the total infections), as well as the time when the daily new infections
decrease to a pre-set value (e.g. less than about 2 new infections per day per
million people), which can be very useful for planning the reopening of
economic and social activities. We use this formalism to predict and compare
these key features of the evolution of the COVID-19 disease in a number of
countries and provide a quantitative assessment of the degree of success in in
their efforts to countain the outbreak.
