Geographic information systems as a part of epidemiological surveillance for COVID-19 in urban areas

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