ABSTRACT
The paper analyzes 3 clusters that differ in the growth rate of Covid-19 from the point of view of the socio-economic structure of the regions of the Russian Federation. In addition, the database also contains clinical indicators characterizing morbidity in the regions, indicators of nosocomial infection, investment parameters and the state of the transport system. Cluster analysis methods was carried out to identify the relationship between socio-economic characteristics of regions. The first cluster is more densely populated, and the regions assigned to the second cluster are removed from each other. Perhaps for this reason, the indicators of the transport system turned out to be less important than socio-economic ones for the spread of infection. The analysis was carried out using machine learning methods based on original methods of optimally reliable partitions and statistically weighted syndromes. The results of comparing the dynamics of Covid-19 spread in clusters 1 and 3, 2 and 3 strongly indicate the importance of studying traffic flows, especially in cities with high population density. The mathematical methods used are an effective tool for the purposes of not only epidemiological analysis, but also for a systematic analysis of the functioning of the socio-economic activity of the population of interacting regions, as well as the role of transport in this process. © 2023 EDP Sciences. All rights reserved.
ABSTRACT
The paper presents a comparative analysis of the transport system of Russia by 12 indicators in accordance with the incidence of respiratory organs according to Rosstat data in 2019 and 2020. Machine learning methods have been applied, namely, data analysis was carried out using 9 available classification methods collected in the Data Master Azforus (DMA) program. In this program "Autoclassing” was carried out, which runs nine available methods on the same training sample. The conducted studies have demonstrated the effectiveness of using machine learning methods to identify patterns linking the health status of the population, including respiratory morbidity, with indicators of the transport system. In the course of the work, a high statistical significance of differences between classes of regions of the Russian Federation, which differ in the dynamics of Covid-19, was obtained by the most important indicators of transport system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The paper presents an original method for solving the problem of finding a connection between the course of the epidemic and socio-economic, demographic and climatic factors. The method was applied to solve this problem for 110 countries of the world using a set of corresponding curves of the COVID-19 growth rate for the period from January 2020 to August 2021. Hierarchical agglomerative clustering was applied. Four large clusters with uniform curves were identified – 11, 39, 17 and 13 countries, respectively. Another 30 countries were not included in any cluster. Using machine learning methods, we identified the differences in socio-economic, demographic and geographical and climatic indicators in the selected clusters of countries of the world. The most important indicators by which the clusters differ from each other are amplitude of temperatures throughout the year, high-tech exports, Gini coefficient, size of the urban population and the general population, index of net barter terms of trade, population growth, average January tempera-ture, territory (land area), number of deaths due to natural disasters, birth rate, coastline length, oil reserves, population in urban agglomerations with a population of more than 1 million etc. This approach (the use of clustering in combination with classification by methods of logical-statistical analysis) has not been used by anyone before. The found patterns will make it possible to more accurately predict the epidemiological process in countries belonging to different clusters. Sup-plementing this approach with autoregressive models will automate the forecast and improve its accuracy. © 2022, The Belarusian State University. All rights reserved.