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

The model's fitting parameters, see Fig, 4 and 5, were obtained by using a normal probability density function. This allows to identify the daily peak with the epidemic center. Due to the fact that after the daily peak, it is possible to have an asymmetric decrease in the number of daily confirmed cases a more real analysis should be done by using skew normal probability density functions. This will be the objective of our future investigations. Curves of the Total Confirmed Cases per Million (TCCpM) of residents for Spain, Switzerland, Italy, Belgium, Austria, Italy, Germany, France, Netherlands, Portugal, the United Kingdom, and South Korea at the 2nd of April (day 72). At the beginning of the disease, we found, for all the countries, a slow increase of the curves. After the initial period, we see a steep increase in TCCpM. For Hubei and South Korea, we also observe that the curves reach a stabilisation point after a while, indicating the decreasing number of new cases per day. The plots show that this stabilisation point has not yet been reached by the European countries at day 72. The possibility to compare the different TCCpM increasing rates for each country is one of the main subject matter of our investigation. The time series of the TCCpM for the European countries of Fig. 1 , Hubei, and South Korea are shown for different days. At day 48, Italy has the same TCCpM of South Korea, at day 52, Italy overtakes and Switzerland reaches South Korea, at day 60, many of the 10 European countries curves are located between the South Korea and Hubei ones, at day 64, Switzerland and Italy overtake Hubei, and, finally, after further 4 days, Spain overtakes Hubei and Italy and reaches Switzerland (overtaking it at day 72, see Fig. 1 ). The updated animated version of this figures is found in the website [8] . We can see that, in the range from 0 to 300, the contagion rate for the European countries is approximately the same. Reaching 500 TCCpM, it becomes evident that Switzerland and Belgium start to increase substantially compared to the other European countries. CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The α-factor, representing the spreading rate of COVID-19, is plotted in (a) for Lombardia, Emilia-Romagna, Piemonte, and Veneto as a function of the TCCpM. From the plots it is clear that the four regions reached the maximum of the α-factor. The bottom figure (b) allows a more detailed look at the α-factor from 0 to 500 TCCpM. We can see that, in the range from 0 to 200, the contagion rate for the Italian regions is approximately the same. After such an initial range, it becomes evident that Piemonte and Lombardia start to increase compared to the other Italian regions. The TRCpM over TCCpM ratio is plotted for Lombardia, Emilia-Romagna, Piemonte, and Veneto. The plots help to complete the analysis of the αfactor done in Fig. 8 . The plots clearly show that for a given TCCpM point, the Piemonte region has a very low ratio of recovered over confirmed cases compared to the other regions. This means that, according to our interpretation, the measuares to contain the outbreak in Piemonte was taken when it was in its initial phase. CC-BY 4.0 International license It is made avail