CORONA VIRUS (COVID-19): summary tables to understand the situation in different nations and Italian regions

Table 1 – Different factors characterizing the selected nations vs latest data of COVID-19 published on 21.03.2020

Last week I read the title of an article published by an Italian´s newspaper “Why are there so many deaths in Italy? There are two reasons, maybe three”. I decided to read it due to the fact that I made myself the same question few days ago as well. After I read it, I was a bit astonished to “learn” that the third reason was the “bad luck”.

Seriously? Is this the only message or information that you want to share with a country who is facing one of the biggest emergencies since decades? Is this the message to help friends, relatives, institutions who are seeking for a solution?

Thus, I decided to do my own research, given the fact that I cannot support and I cannot stand statements like the root cause is “bad luck”. It does not fit with my attitude to life. It does not fit to my attitude as a researcher.

I decided to analyze both the Italian situation and other selected Nations, in order to have a global vision, even if not accurate because I could not analyze cases by cases on a local base.

Table 1 is the summary of the findings already published along the past week in the different articles (some in Italian, some in English):

http://www.emigrantrailer.com/2020/03/16/corona-virus-covid-19-contagions-spread-and-deaths-in-italy/

http://www.emigrantrailer.com/2020/03/19/corona-virus-covid-19-age-smoke-alcohol-obesity-and-health-among-different-nations-and-italian-regions/

http://www.emigrantrailer.com/2020/03/21/corona-virus-covid-19-correlazione-con-concentrazione-di-pm10-e-pm2-5-nellaria/

The table contains a chromatic classification to facilitate the understanding of the severity of each analyzed factor. At the right side of the table, I report the latest official data published by the World Health Organization.

The table should be self-explicative.

For all the analyzed factors, Italy does not show any “Good” or “Very Good” results. This is a fact. No bad luck at all.

Iran shows almost “Very Bad” results for all the factors: only smokers, population density and age over 65 are much better and this can explain the lower death rate compared to Italy. In fact, it has been already published this week that the average age of death people in Italy was around 80 years.  

China shows a mixed situation with some parameters “Very Bad” and other “Good”. Spain shows a similar situation like the Italian one. But it has very good values for air pollution and a much lower population density: this helps significantly to limit the contagion´s spread.

France and Japan have similar rate of deaths, but very different values of the analyzed factors. This supports the idea that we must think in a context of “combination of factors”. Namely, for instance, even if Japan has a high population density and the highest percentage of over 65, it shows much lower values for health parameters like obesity, overweight, wine consumption. Simultaneously, France has “very bad” rating for the latter three health parameters, but it has very low values for population density, and over 65 compared to Japan.

Germany, one of the countries with lowest death´s rate, shows very good results for health expenditure pro capite and for air pollution. Switzerland has more or less a similar scenario. Korea has a high population density and high values for air pollution. However, these factors are well balanced by the age, overweight, obesity and wine consumption factors. For all remaining countries, similar consideration can be done and everybody can draw the conclusions.

My conclusion and recommendation is that we must stop looking for ONE single factor to explain the status quo of the different countries. We need to consider more than one factor.

In parallel to this analysis, I also looked carefully to the published data of the different Italian regions. Differently to the factors in Table 1, I also added other factors: average age, public and private health infrastructure for 1000 inhabitants and Gross Domestic Product (GDP) (note for my Italian friends: this is the PIL=Prodotto Interno Lordo) (see Table 2).

Table 2 – Different factors characterizing the Italian regions vs latest data of COVID-19 published on 21.03.2020

Personally, I found again the table self-explicative.

In particular for Lombardy: it is by far the most populated area in Italy, it has the second population density, it has a high percentage of over 65 like all other regions, it has a high percentage of overweight and obesity like all other regions, it has a high number of smokers, it has the second last value for public health infrastructure for 1000 inhabitants, it has the highest values of annual PM2.5 values.

Moreover, after some discussions in the last hours, I also analyzed the Gross Domestic Product (GDP) which somehow is a parameter to indicate the economic activities within a certain area. Why is this factor important? Well, a high value indicates a good economic status of the area, reflected by several economic exchanges and activities. Similarly to the most efficient transport system (a factor analyzed in my first article), the GDP can explain the differences between neighboring areas like Veneto and Emilia Romagna. In fact, so far it is the only factor that differs significantly among these two regions, which are part of that area (the Pianura Padana) where the virus initiated to spread.

What are the proposed solutions after this analysis? Nothing new to what China did already long time ago with the city of Wuhan:

  • Lock down the areas (a real lock down…not mild as seen in these weeks) for the needed time;
  • Lock down or dramatically reduce (maybe implementing a shift plan) the economic activities and trades in this area for the needed time;
  • Build up a temporary hospital in the most affected areas (why is this measure not yet implemented?);

I know. It is easier for me to write these three points. But the national and local government in the Italian regions should implement unpleasant measures if they really want to improve the current situation.

Finally, I am well aware that a proper root cause analysis can be executed only when the emergency is finished and all the singular cases are analyzed. However, I asked myself in these days: do we really have time to wait for this moment?

This entry was posted in COVID-19 (Corona Virus) and tagged . Bookmark the permalink.

6 Responses to CORONA VIRUS (COVID-19): summary tables to understand the situation in different nations and Italian regions

  1. Lorenzo says:

    Andre sei un mito, lavori, ti alleni, Sei fidanzato e trovi pure il tempo per ste analisi. Non ho parole

    • admin says:

      Grazie Lorenzo! Diciamo che l´ultima settimana ho fatto zero attivita´ sportive e che la fidanzata l´ho un po´trascurata, rischiando la vita! 🙂 Ho cercato di semplificare le tabelle in modo da essere il piú comprensibile possibile.

  2. Salvatore says:

    Grande Andrea…come fai a portare avanti tutte queste attività impegnative! In bocca al lupo!

  3. Roberto Di Latte says:

    Bella Ricerca complimenti Andrea

Leave a Reply

Your email address will not be published. Required fields are marked *