Comparing confirmed cases plotted vs. time

In Fig. 1 we show an interactive chart reporting on data for Germany (DE), United States (US), Spain (ES), France (FR), United Kingdom (UK), and Italy (IT). The total cumulative number of confirmed cases for each nation is plotted vs. time.

The cells in the dashboard on the left side of the plot contains values that can be set by the user by digiting numbers in. The “Factor” column allows the user to rescale the vertical axis by the chosen factor, or to completely remove the country from the plot by digiting zero. The “Shift” column allows to apply a positive shift in terms of calendar days. The Y/N cell allows to switch the vertical axis from total cases per country (N) to cases per million inhabitants (Y).

What do we learn from these graphs?

By leaving the default values (i.e. all Factors = 1, all Shifts = 0), the plots allow to observe the trends of different countries, their remarkable overall similarities and their minor but important differences. We observe that Italy has been the country with most confirmed cases among the ones considered here, both in absolute (a) and relative (b) terms, until late March. Since then, the United States became the country with most cases in absolute terms, and Spain in relative terms.

By acting on the dashboard, the user can exploit the remarkable similarities in the shapes to shift and rescale the plots of different countries, until the plots overlap. This allows us to attempt some empirical, prudent estimates about the evolution of the countries that are later in the epidemics, based on what happened to the ones that started earlier.

Comparing confirmed cases resorting to a time-independent plot

In Fig. 2, the percental daily growth of confirmed cases is plotted vs. the total number of confirmed cases. The case of a country that so far contained the spread of Covid-19 much more efficiently than all Western countries, South Korea, is shown for comparison.

What do we learn from this graph?

We first observe that this is not a time-dependent plot: the time variable has been removed. It shows how efficiently different countries succeed to stop their national epidemics (i.e., get to zero daily percental growth) before getting a too large number of cases. Again, while some comments are posted below, the user is encouraged to use this plot following his/her own ideas.

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