RESUMO
A phenomenological analysis of the time evolution of some COVID-19 data in terms of a Fermi-Dirac function is presented. In spite of its simplicity, the approach appears to describe the data well and allows to correlate the information in a universal plot in terms of non-dimensional or reduced variables Nr = N (t)/Nmax, and tr = t/{Delta}T, with N (t) being the total number of cases as a function of time, Nmax the number of total infected cases, and {Delta}T the diffuseness of the Fermi/Dirac function associated with the rate of infection. The analysis of the reported data for the first outbreak in some selected countries and the results are presented and discussed. The approach is also applicable to subsequent waves. Support of our framework is provided by the SIS limit of the SIR model, and simulations carried out with the SEICRD extension.