Application of spatial analysis of morbidity and mortality from COVID-19 (the case of the Pskov region)
Apart from biomedical and organizational issues, the emergence of the new coronavirus COVID-19 (SARS-CoV-2) pandemic, set large-scale tasks for creating and improving mathematical and information technologies that operate spatial data in statistical analysis and forecasting. The regional level is seen as a suitable choice for spatial analysis of COVID-19 morbidity and mortality due to the availability of statistics, as well as data on geographical patterns, characteristics of the distribution space (population density, concentration in one city, density of the transport network, distance to the focus of the disease, etc.). The case of the Pskov region shows that the regional healthcare system experiences a significant shortage of personnel and a noticeable lack of resources. When assessing the existing and prospective healthcare infrastructure, it is advisable to take these points into account while developing an effective, evidence-based healthcare policy. The article shows that graph-based models are more likely to be efficient for adequate modeling at the interregional and regional level, while the geographical distribution of patients should be taken into account for the analysis of processes in individual settlements.