A technique for forming, clustering and visualizing so-called correlation networks of countries determined by the dynamics of the COVID-19 pandemic is here proposed. The links between countries as nodes of such networks correspond to the values of correlations between sets of parameters corresponding to the dynamics of the pandemic in these countries. To build network structures for each node (country), vectors are formed - arrays of numbers corresponding to the dynamics of the pandemic (in one case - the dynamics of daily mortality, in the second - the dynamics of infection). For this purpose, data obtained from an external source - an aggregator of such data - is used. This approach, in contrast to the existing ones, has such advantages as a relatively low dimension of vectors-parameters corresponding to countries; a reliable mathematical basis for correlation analysis; objectivity - for the "purity" of data corresponds to a reliable data aggregate; the use of standard software tools; and the relative ease of implementation. This method can be used in analytical systems for various purposes to analyze arrays of entities without explicit relationships between them. Correlation networks can be considered as the basis for constructing probabilistic networks and applying fuzzy semantic network technologies for further analysis with the use of experts, and decision support systems. Keywords: Big Data, Pandemic Dynamics, Data Aggregation, Network visualization, Cluster Analysis, Modularity JEL Classification: I10, Y10, Z18 Distributed in Human Health & Disease eJournal Vol 4, Issue 108, August 11, 2020. Distributed in Coronavirus & Infectious Disease Research eJournal Vol 1, Issue 104, August 21, 2020.