Cluster analysis of the association of COVID-19 mortality time series with pandemic intervention measures

The undisputed assumption during the COVID-19 pandemic was that the isolated pathogen SARS-COV-2 was spreading worldwide at lightning speed with the same mortal effects across the world. Under this assumption, synchronized death time series should be expected, however previous results show that COVID-19 mortality time series fall tino very definitive clusters that remain stable under various feature extraction and clustering approaches. Hence, this assumption needs to be reexamined. The research question in this paper is the existence of associations between the intervention measures, namely the social control measures and the massive vaccination programs, and the actual COVID-19 mortality time series. To this end we carry out the spectral clustering of the countries over each of these time series after dimensionality reduction by non-negative matrix factorization (NNMF). Then we look for the maximal intersection between clusters based on each intervention measure and mortality based clusters. The rationale is that clusters of countries based on intervention measures (Vaccination doses, Stringency Index) should greatly overlap with mortality clusters if these measures had any influence on mortality due to COVID-19. Hence, the size of these intersections can be interpreted as a measure of the association between intervention measures and mortality. After exhaustive exploration, the results are disappointing, showing that there is little association between Stringency Index policies and COVID-19 mortality outcomes. Also, we found little association between Vaccination doses and COVID-19 mortality outcomes.