Possibilities of Using Geoinformation Technologies in Epidemiological Surveillance of COVID-19 Infection at the Regional Level

Relevance. The spread of the new coronavirus infection throughout the world has led to expressed interest in studying, among other things, the patterns of territorial distribution of cases of the disease. Aim. To investigate the spatial distribution of cases of COVID-19 infection and develop proposals for the use of GIS technologies in the epidemiological supervision system for the new coronavirus infection at the regional level. Materials and methods. The study was conducted on the territory of the closed administrative- territorial entity of Zelenogorsk, Krasnoyarsk Territory. 4176 cases of COVID-19 infection were reported during the study period of 57 weeks (04/12/2020 to 06/18/2021). Each case of the disease was geocoded by the residence of the sick person using a projection coordinate system from the open data of the Open Street Map resource. The spatial distribution of COVID-19 cases was studied with geographic information system QGIS Desktop version 3.28.0. Spatial autocorrelation analysis was carried out using the Getis-Ord index. Results. During the application of GIS technologies, the density of distribution of COVID-19 infection cases was estimated, six zones with an average core density were discovered, the outbreaks in the northern part of the city had the greatest epidemiological significance. When assessing the clustering of cases within the specified territorial zones, eleven clusters were identified, three of which were characterized by the highest density of cases - 1210.1 cases per 1 sq. km, 1155.9 and 1116.7 cases per 1 sq. km. The Getis-Ord index value ranged from 0.00 to 2.576, the majority of cases was recorded in territorial clusters located in the northern part of the city. Conclusions. New knowledge obtained on the basis of modern GIS technologies about the presence of “hot spots” or clusters in the administrative territory will make the adjustment of preventive measures in micro-areas with a high prevalence of infection possible and develop strategies for more effective control of COVID-19 infection.