Abstract

Introduction
It is essential to understand, on a large geographical scale, the dimension of the COVID–19 pandemic by identifying the most affected countries, knowing that all the world is suffering an unusual disruption regarding several health impacts, but also heavy economic, financial and social effects. A key role is reserved to Data Science to understand the present and to deepen a prospective analysis at COVID–19 day after.

Objective
The main objective of the present study is to describe the COVID–19 prevalence in EU and five other OECD countries using five epidemiological variables. Secondly their association with non–pharmaceutical measures taken in some countries to control and attenuate the evolution of the epidemic was analyzed.

Methods
The COVID–19 study covers twenty–six EU countries and additionally Switzerland, Norway, Turkey, Israel and United Kingdom. Five epidemiologic variables were analyzed by 100.000 inhabitants at the beginning of May 2020: total number of cases, total number of deaths, total number of active cases, total number of critical or serious cases and total number of tests. Also, eight non–pharmaceutical measures were selected for association purposes. A multivariate statistical exploratory approach with principal components, hierarchical and non–hierarchical (k–means) cluster analyses was applied.

Results
A COVID–19 prevalence typology of four country clusters was identified regarding EU countries and five OECD countries on early May. In the two clusters, with a total of ten countries where the pandemic seemed to evolve more seriously, different patterns regarding the number of tests are observed. Two other clusters, with 12 and 9 countries, show an intermediate or low prevalence but differences in testing patterns. For EU countries of both clusters more affected, COVID–19 containment strategies were studied considering three modalities of implementation timing for eight non–pharmaceutical measures. The three different behaviors mirrored the clusters findings. Countries previously classified into cluster 1 appear together again, as do countries belonging to cluster 2. In spite of a common behavior for some measures, generally countries of cluster 2 implemented other interventions later in time. Sweden is a “special case”, taking just a few of these measures, most of them later than other countries.

Keywords

COVID–19, epidemiological variables, non–pharmaceutical measures