These findings are shared for research purposes and indications for decision makers to help them broaden perspectives and expand understanding until they materialize in a reviewed paper.

Socio-economic Factors


For social variables, we have looked at two sets of data; demographic data (Density per km2, Average daily ridership for cities with a metro, the GINI index of income distribution and the GDP per Capita) and mobility data (changes in frequenting of residential, workplaces, parks, grocery/pharmacy, retail/recreation and transit stations).


Demographic data


The following correlations (and significance levels) have been found.

Positive correlation with case count:

      Average daily ridership: 0.73, sig at 0.01

      Density km2: 0.61, sig at 0.05

      GINI Index: 0.57, sig at 0.10


Positive correlation with death count, sig at 0.10:

      Average daily ridership: 0.58

      Density km2: 0.52

Positive correlation with cases per 100k, sig at 0.10:

      Density km2: 0.57


This shows that increases in ridership and higher population densities are associated with both higher infection rates and deaths and that higher income inequality (as measured by Gini) is associated with a higher number of cases.


In developing countries, the epidemic seems to hit harder wealthier populations first, whereas in Europe and United States it seems to hit more unfavoured populations. Detailed data is not available for further analysis.



Mobility data

Using Google mobility data, we have found that there is a statistically significant  systematic correlation in most countries at +25 lag between the transit stations presence and deaths in locations where there's a high death count of COVID-19.

This pattern also applies on workplaces including in countries whith mild or no lockdow. 

Metro/Subway ridership is often an indication of office concentrations in modern buildings with shut windows possibly recycled air or A/C where clusters may form. So actually both variables may correlate.


So it is hard to account that metro mass transportation could have contributed to the high infection rate or is it office buildings or a combination (as there’s high correlation between daily death count and cases count).

Significance was set at 0.05df = 25 that correlation coefficient should be > |0.22| 


To a lesser extent a similar pattern applies to retail and recreation. This is altered in the sense that most recreation and retail had been closed in almost all countries and restricted in Sweden.


All five hardest hit cities were dense and had a dense subway (London, New York, Madrid, Brussels, Milan, Paris)




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