First wave of SARS-COV2 in Europe: Study and typology of the 15 worst affected European countries

Abstract

Since 11 March 2020 when officially declared a global pandemic, Covid-19 (or SARS-COV2) has turned out to be a multifaceted disease differently affecting countries and individuals. What makes certain countries more vulnerable than others has attracted the interest of scientists from various disciplines. This paper intends to compare the impact of demographic parameters, population health conditions and policy actions on prevalence and fatality levels of Covid-19 during the first 3 months since its declaration of global pandemic. A country-level exploratory analysis has been conducted in order to assess how demography, national health conditions and measures taken interact and condition the disease outcomes. Analysis relies on publicly available data on Covid-19 reported cases, deaths and number of persons tested. Those data are combined with demographic parameters (sex ratio, mean age, population density and life expectancy), health data (cardiovascular death rate, diabetes prevalence, share of smokers among males and females and number of hospital beds) and information about relative national policies aiming the management of the pandemic (lockdown timing and duration). Our analysis confirms the diversity of factors and the complexity of their interaction in explaining the propagation and fatality of the disease across Europe. Our findings question some well-established attitudes concerning the role of demographic variables and public health conditions in the spread of the disease.

Keywords: Covid‐19; Europe; age; cardiovascular death rate; demography; pandemic; policy measures; sex ratio.

ILLUSTRATION 1

Excess mortality during the first wave of the SARS‐COV (compared with 2019) in provinces, departments and districts of various countries in Europe. Sources: EUROSTAT, and compilations by European Data Journalism (https://www.europeandatajournalism.eu), CSO (Ireland, Central statistics office), EODY (Greece, Εθνικός Οργανισμός Δημόσιας Υγείας)

Figure 1

Covid‐19 fatality indexes in European countries, as of 20/06/2020. Note: Left axis refers to the number of deaths per million inhabitants (in columns); right axis refers to the Crude Fatality Rate (in dots) defined as the number of deaths over the number of confirmed cases

Figure 2

Correlation between demographic parameters (sex ratio and share of above 70 years of age) and Covid‐19 mortality rates (per 1M of population). Note: Vertical axis refers to the number of men for every 100 women; horizontal axis refers to the share of above 70 in total population. Each dot refers to a country; the colour of each dot illustrates how high the number of Covid‐19 deaths/million persons is. In bold the 15 hardest hit European countries (as mentioned in our analysis)

Figure 3

Correlation between living standards (GDP per capita & life expectancy at birth) and Covid‐19 mortality rates (per 1M of population). Note: Vertical axis refers to life expectancy at birth (both sexes); horizontal axis refers to the per capita GDP at purchasing power parity (in constant 2011$). Each dot refers to a country; the colour of each dot illustrates how high the number of Covid‐19 deaths/million persons is. In bold the 15 hardest hit European countries (as mentioned in our analysis)

Figure 4

Correlation between underlying health issues (diabetes prevalence and cardiovascular death rate) and Covid‐19 mortality rates (per 1M of population). Note: Vertical axis refers to diabetes prevalence (as share) across population aged from 20 to 79 (in 2017); horizontal axis refers to cardiovascular death rate (in 2017). Each dot refers to a country; the colour of each dot illustrates how high the number of Covid‐19 deaths/million persons is. In bold the 15 hardest hit European countries (as mentioned in our analysis)

Figure 5

Dendrogram of a hierarchical ascendant classification of 15 hardest hit European countries in respect to their demographic variables. Note: Countries have been classified in respect to the following variables: population density, sex ratio, share of above the age of 70 and life expectancy. Clustering method here used was the nearest neighbour combined with the squared Euclidean distance

Figure 6

Key trends in 15 selected countries. Note: Countries are ranked in respect to the number of deaths per million inhabitants (in descendant order) as registered on 20 June 2020

Figure 7

Dendrogram of a hierarchical ascendant classification of the hardest hit European countries who decided strict lockdowns in respect to measures taken to halt the pandemic. Note: Countries have been classified in respect to the following variables: number of tests per thousand inhabitants, duration of lockdown, numbers of cases and deaths (per standardised population, see Table 1) on the lockdown date. Clustering method here used was the average between groups linkage combined with the squared Euclidean distance. Sweden and Switzerland have been excluded from this clustering for they never proceeded with a total lockdown