Open AccessGeographic information systems as a part of epidemiological surveillance for COVID-19 in urban areas
Author(s) -
А. И. Блох,
Н. А. Пеньевская,
Н. В. Рудаков,
О. А. Михайлова,
А. С. Федоров,
А. В. Санников,
С. В. Никитин
Publication year - 2021
Publication title -
fundamentalʹnaâ i kliničeskaâ medicina
Language(s) - English
Resource type - Journals
eISSN - 2542-0941
pISSN - 2500-0764
DOI - 10.23946/2500-0764-2021-6-2-16-23
Subject(s) - scan statistic , geography , geographic information system , covid-19 , pandemic , cartography , cluster analysis , distribution (mathematics) , spatial epidemiology , epidemiology , statistics , medicine , mathematical analysis , mathematics , disease , pathology , infectious disease (medical specialty)
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.