2020/04/17 by E. Daddi, Mauro Giavalisco, Daddi, Emanuele +1
Economics, Econometrics and Finance · Mathematics · #COVID-19 Pandemic Impacts #COVID-19 epidemiological studies #Complex Systems and Time Series Analysis #FOS: Biological sciences #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2004.08365
openalex publication_date 2020/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We discovered that the time evolution of the inverse fractional daily growth\nof new infections, N/dN, in the current outbreak of COVID-19 is accurately\ndescribed by a universal function, namely the two-parameter Gumbel cumulative\nfunction, in all countries that we have investigated. While the two Gumbel\nparameters, as determined bit fits to the data, vary from country to country\n(and even within different regions of the same country), reflecting the\ndiversity and efficacy of the adopted containment measures, the functional form\nof the evolution of N/dN appears to be universal. The result of the fit in a\ngiven region or country appears to be stable against variations of the selected\ntime interval. This makes it possible to robustly estimate the two parameters\nfrom the data data even over relatively small time periods. In turn, this\nallows one to predict with large advance and well-controlled confidence levels,\nthe time of the peak in the daily new infections, its magnitude and duration\n(hence the total infections), as well as the time when the daily new infections\ndecrease to a pre-set value (e.g. less than about 2 new infections per day per\nmillion people), which can be very useful for planning the reopening of\neconomic and social activities. We use this formalism to predict and compare\nthese key features of the evolution of the COVID-19 disease in a number of\ncountries and provide a quantitative assessment of the degree of success in in\ntheir efforts to countain the outbreak.\n