The Constant K and the Gaussian Temporal Evolution for COVID-19

Abstract

We present the Gaussian temporal evolution of Corona Virus, the temporal average constant K_{temporal average constant} = K_t. The K_t and its standard deviation come from the analysis of 52 experimental Gaussian distribution -histogram-. We have analyzed all histograms from 185 countries presented in the reference (Johns Hopkins, 2020), and, we found 52 countries have a definite trend towards an experimental Gaussian profile. As a result, we found Kt = K_{52 countries} = (35 ± 5) days - average & standard deviation-. We also calculate using an experimental Gaussian got in reference (Johns Hopkins, 2020), the temporal evolution for the world, the constant K_world. We found K_world = K_w = (47 ± ½) days. Finally, up to this date, 20 April/2020, we have only 52 of 185 countries presenting the trends towards an experimental Gaussian profile (Johns Hopkins, 2020). The main conclusion from this short communication is that the standard deviation found -K_t = (35 ± 5) days-, is very low, which is very good. Therefore, we may conclude the maximum spread of the Corona contamination will occur in a maximum of up to 40 days from the first registered contamination and, in the worst scenario, up to 30 days. Regarding the Brazil peak of contamination, on 10 March/2020, we carried out A PREVISION, and, in that time, we have affirmed by reasoning, the peak in Brazil would be around 10-15 April/2020. Up to this date, there is a confirmation of this prevision. (Johns Hopkins, 2020). For our next prevision, the decrease from contamination must trend to zero among 30-40 days after the peak contamination. These are the most critical situation faced because the real zero takes a while to get to null and, if no personal safety such as social reclusion is adopted, the contamination starts all over. Finally, we have observed the K_w has a shifting in the function of the time and, our finding explains this.