Abstract
In large metropolitan areas, there are districts with a high number of reported cases of airborne diseases. The occurrence of tuberculosis, COVID-19 and pneumonia cases in the same spatially limited territories can be a marker of epidemic and social processes. The purpose of this study is to develop a comprehensive geoinformation model for the spread of three aerogenic diseases to support the quality of surveillance, and to assess the possibilities for predicting the development of the epidemic process in a megalopolis. Materials and methods. 6,111 places of residence and registration of tuberculosis patients, 33,270 cases of COVID-19 and 33,346 places of residence of patients diagnosed with pneumonia were geocoded in St. Petersburg. All spatial data were saved in GeoPackage format in the EPSG 4326 coordinate system. Statistical spatial analytics was performed using the GeoDa and NextGIS software. Absolute values have been standardized (normalization method — Z-score). The results of statistical analysis using the local Goethis-Ord and Moran index were transformed into thematic maps using NextGIS GIS. Territories (clusters) with high values of standard deviations in the number of cases of the disease and the spatial connectivity index were classified as high-risk areas of the disease, and vice versa. To assess the social characteristics of the territories, data on the price of housing were used, 1,596 advertisements for sale were analyzed: 1,049 residential apartments and 547 individual houses. As a result of the conducted research, a comprehensive geoinformation model of the spread of three diseases across the municipalities of St. Petersburg has been created. There is a positive spatial relationship between the number of cases of tuberculosis and COVID-19, and a number of territories are simultaneously located in clusters with high index values for various diseases. Statistically significant differences between the cost of apartments and residential housing in territories with high and low risk of the studied respiratory diseases indicate the existence of an economic and social nature of clustering territories by disease risk.