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
This paper introduces TITANIS, a new social media text analysis tool specifically designed to assess the reaction of social media users to global events from a psycho-emotional point of view. The tool offers an expanded set of text parameters and natural language processing methods suitable for working with texts from social media. In addition to the widely used NLP approaches, such as tf-idf and sentiment analysis, TITANIS includes psycholinguistic, semantic, discursive, and other types of analysis that allow detecting more peculiarities in the texts of users with different psycho-emotional states. The paper describes the structure of the tool and provides insight into the methodological background of its functionality. To demonstrate some capabilities of TITANIS, we applied it to the Pikabu data to analyze the user reaction to the period of self-isolation and the COVID-19 informational background on social media. © 2021, Springer Nature Switzerland AG.