ГЕОИНФОРМАЦИОННЫЕ СИСТЕМЫ КАК ИНСТРУМЕНТ ИЗУЧЕНИЯ НЕРАВНОМЕРНОСТИ РАСПРЕДЕЛЕНИЯ СЛУЧАЕВ COVID-19 В ГОРОДСКИХ УСЛОВИЯХ

Aim. To identify clustering areas of COVID-19 cases during the first 3 months of pandemic in a million city. Materials and Methods . We collected the data on polymerase chain reaction verified cases of novel coronavirus infection (COVID-19) in Omsk for the period from April, 15 until July 1, 2020. We have drawn heat maps using Epanechnikov kernel and calculated Getis-Ord general G statistic (Gi*). Analysis of geographic information was carried out in QGIS 3.14 Pi (qgis.org) software using the Visualist plugin. Results . Having inspected spatial distribution of COVID-19 cases, we identified certain clustering areas. The spread of COVID-19 involved Sovietskiy, Central and Kirovskiy districts, and also Leninskiy and Oktyabrskiy districts a short time later. We found uneven spatiotemporal distribution of COVID-19 cases infection across Omsk, as 13 separate clusters were documented in all administrative districts of the city. Conclusions . Rapid assessment of spatial distribution of the infection employing geographic information systems enables design of kernel density maps and harbors a considerable potential for real-time planning of preventive measures.