Research background: The authors participate in the creation of a model for monitoring and predicting the behavior of autonomous systems on a selected infrastructure for the analysis of current phenomena. Covid 19 dampens globalization trends and processes, especially free movement. The primary research aim was to identify changes in Covid19 indicators in area. The secondary aim was to find agreement in the behavior of selected globalization factors. Purpose of the article: For the presented analysis, a research question was expressed how the EU states reacted to the change of local extremes of the pandemic. The paper presents spatial changes in the number of infected and dead in EU countries over time and compares these changes with selected changes in population movements and changes selected economic indicators. Methods: Notably daily, monthly and quarterly data from Eurostat, OECD, ECDC and WHO at the level of EU countries were used for the analysis. Local extremes were identified by comparison, precedence analysis, structural analysis and simulation. Findings & Value added: The added value of the paper lies in the chosen method, which identifies local extremes using structural analysis in a geospatial context. In most cases, global analyzes fail to take into account the links between the analyzed factors and the geopolitical location of the region. The work presents the possibilities of analysis using precedent modeling, through which analyzes can be performed with respect to geographical links. The output is the identification of EU countries according to responses to changes in pandemic factors.