Fig. 1a shows the temporal evolution of the cumulative number of confirmed cases for the selected regions (Lombardia, Veneto, Piemonte Emilia Romagna, Marche, Campania, Lazio, Liguria, Abruzzo). In Fig. 1b, the same data are normalized per million inhabitants. The cells in the control table contain values that can be edited by the user. The “Factor” column allows the user to rescale the vertical axis, multiplying it by the entered value, or to make the region disappear from the chart by entering the value 0. The “Shift” column allows to apply a positive translation in terms of days.
What do these charts tell us?
Leaving all the default values unchanged (Factors=1, Shifts=0) these graphs provide an overview of the trends in the different regions. The first graph clearly shows that northern Italy, and in particular Lombardy, Veneto, Piemonte and Emilia Romagna, is the most affected part of the country. Lombardy is by far the region with the most cases and also the one where the epidemic began before its spread. The normalized data better highlight the extent of the epidemic also in Marche and Liguria. Among the regions considered, Campania has the lowest number of deaths per million inhabitants.
By changing the values in the table, the user can highlight the striking similarities in the shape of the curves, by translating and/or scaling the data until they overlap. This makes it possible to obtain some estimates, to be considered prudently, regarding the evolution of the epidemic in some regions, on the basis of what happened in those previously affected by the spread.
An example can be obtained in Fig.1b by putting F = 0 for all regions except Emilia Romagna and Lazio and applying to the curve of Emilia Romagna (data referred to April 22) a scale factor of F = 0.21 and a time translation of S = 5 days. This produces a very good overlap with the curve of the Lazio region. If this trend is respected, in 3 days the number of confirmed cases per million inhabitants in Lazio will be about a quarter of what Emilia Romagna now has.
Fig. 2 shows the trend of the daily percentage increase in confirmed cases based on the total number of confirmed cases for different Italian regions. The Italian regions have been selected with the most significant spread of the contagion, in conjunction with the regions where there are operational units of our Institute.
We first observe that this is not a time-dependent plot: the time variable has been removed. It shows how effectively in absolute terms the various regions have managed to contain the spread of the epidemic (zeroing out the daily growth rate of cases) before reaching a too large number of confirmed cases.
Fig. 3 shows the same daily trend but as a function of the number of confirmed cases per million inhabitants for each individual region. Again, the graph allows to compare the containment effectiveness of each region but correlates it with the percentage of cases found with respect to the population of the inhabitants. Regions with equal daily growth and fewer confirmed cases per million inhabitants are in a better state of combating contagion.
Comments (data from April 20):
All regions follow a more or less similar pattern with numerous irregularities most likely related to the way data is acquired (delays in the processing of swabs, lack of information from health institutions (RSA), etc.). For example, in Fig. 2we recognize three separate groups of behavior in crossing the 10% daily increase threshold: the regions of Central-South plus Liguria cross this level with less than 3000 cases, the northern regions with more than 5000, Lombardia alone with more than 25000. However, if this trend is considered in relation to the population of each individual region as in Fig. 3, we find a greater homogeneity of behavior. With the exception of Campania, Lazio and Abruzzo, which perform better, in all other regions cases are around 1000 to 2000 per million inhabitants.