Analysing and comparing the COVID-19 data: The closed cases of Hubei and South Korea, the dark March in Europe, the beginning of the outbreak in South America

Stefano De Leo; Gabriel Gulak Maia; Leonardo Solidoro

where T EE is the TCCpM at the end of the outbreak, X EC the Epidemic Center, and σ is the Standard Deviation [9] . Using the software Wolfram Mathematica [10], we applied this model to the TCCpM data, as well as to the total number of recovery cases, using then the builtin function FindFit to find the best fitting parameters T EE , X EC , and σ as a function of time. This was done by starting with data from only a few days after the first day of the outbreak and then calculating the best fitting parameters for the data of that time frame. After that, the data is updated on a daily basis and a new set of best fitting parameters is found for each day. After enough days, we have a graphic representation of how the fitting parameters changed as new data became available. At first, the fitting parameters vary in an oscillatory way with time, becoming, eventually, stable. When their values stabilise, enough data has been collected to provide more precise fitting parameters, that are now capable of better describing the TCCpM curve by with Eq. (2).