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

Abstract

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.