The transmission of the SARS-CoV-2 virus is a complex and intricate process, but it is possible to efficiently track and contain the spread of the pandemic in a given area by observing the regularities of the pathogen’s diffusion. One of the basic measures to hamper the development of the disease was to reduce the intensity of social contact by banning free movement. An adequate response in selected regions, where the virus develops much more rapidly, is crucial and prevents serious economic damage to many industries. The modern perception of cartography as an interdisciplinary tool can contribute to limiting the diffusion of infection through spatial analyses. The aim of this paper is to present cartography as a tool to support the “management” of a pandemic. In terms of methodology, the well-known choropleth map method was employed along with spatial structure analyses. The basic category considered within the framework of statistics and econometrics is spatial relationships formulated for the purpose of achieving the set objective in the form of spatial weight matrices. In the analyses presented here, a modified Moran model was used, within which the Authors applied a row-standardised weight matrix using migration data of individual counties. The paper reviews what has been achieved so far, based mainly on European solutions. Insufficient availability of reliable data needed for advanced models (especially in the initial phase of virus spread) means that using migration data from the nearest neighbourhood can be a viable solution. This approach comes down to an analysis of migration and the population density in the county in question. A simplified analysis with a statistically significant probability allows the identification of counties that could potentially become sites of uncontrolled virus transmission in areas of high population density and high mobility. This is undoubtedly the main achievement of the publication. The results obtained converge approximately with the actual development of a pandemic. The studies carried out indicate that the development of a pandemic is influenced not only by the number of infections, but above all by population density, as well as economic, social, educational and transport networks, as shown by the high Pearson coefficient correlation of 0.83. The analyses indicate the possibility of uncontrolled transmission of the virus in areas of high population density and high mobility.