On the clustering of countries responses against COVID-19 using multivariate epidemiological data
Murat Razi, Manuel Graña · Procedia Computer Science · 2025
Retrospective analysis of the data gathered during the COVID-19 pandemic can be used for pandemic preparation in the future. It should be now possible to ascertain if the results of the pandemic policies have been the same across the world. Detecting differences in responses over time can be useful for preparation for future pandemics by posing questions on the causes for these different responses. In this direction, this paper contributes evidence that the pandemic response and the results achieved were not the same everywhere. We consider the multivariate time series of deaths, new people vaccinated, and stringency index for each country. Multivariate Dynamic Time Warping (DTW) allows the elastic matching of this multivariate time series, that results in a similarity measure between countries given by the cost of the elastic matching. Clusters of countries with similar pandemic measures and death time series results can thus be detected by Hierarchical Clustering. We find a cluster composed of some western European countries that is robustly detected under various conditions.