Topological analysis of COVID-19 wave patterns and policy responses in Europe.

The COVID-19 pandemic critically exposed public health vulnerabilities and the intricate challenges of international policy coordination. While extensive research has independently explored infection rates and governmental responses, a significant gap persists in understanding how structural similarities in pandemic dynamics relate to regional, geographic, or economic interdependence. Addressing this crucial gap, this study employs Topological Data Analysis (TDA), specifically the Mapper algorithm, to analyze normalized COVID-19 case data up to the end of 2022, segmented into epidemiological waves, across 15 European countries. We integrate this analysis with both average and wave-specific policy stringency scores and established group membership data (for example, Eurozone, Schengen Area, Visegrád Group) to identify clusters of nations exhibiting analogous wave profiles. Furthermore, geographic neighbor relationships are incorporated to examine the intersection of spatial and dynamic proximity. Our findings demonstrate that topological similarity in pandemic trajectories does not consistently correlate with formal economic or geographic affiliations, and countries can appear in multiple clusters, revealing transitional epidemic profiles. Statistical analyses reveal no significant variation in policy stringency among these clusters, suggesting that shared economic frameworks did not inherently drive coordinated pandemic responses. Robustness checks using alternative wave segmentations and daily stringency data confirm the stability of these results. These results are critical for understanding the complex, multi-faceted nature of national pandemic responses and provide a novel framework for assessing the effectiveness and alignment of public health strategies beyond traditional geopolitical boundaries.