Two-scale Phenomenological and Statistical Analysis of the COVID-19 Pandemic

Abstract In this work, the daily reported new infection cases and deaths globally due to COVID-19 are approximated by Gaussian functions of the time elapsed since the beginning of the pandemic. This approximation is shown to consistently designate the time evolution of daily rates of new cases and the total duration of the epidemic in any given region or country. At the pandemic scale, this phenomenological analysis is applied for condensing statistical data of the epidemic evolution in twenty-four countries into two “universal master plots” involving in total five adjustable parameters per country. The analysis reveals intrinsic features of the pandemic and local specificities of the epidemic as well, helps in anticipating the epidemic peak date in each country and yields confidence intervals for the average numbers of incidences. Finally, the phenomenology facilitates comparisons of the epidemic evolution in different countries and evaluations of the effectiveness of related health policies.