Socio-economic factors in the spread of SARS-COV-2 across Russian regions

Синицын, Е. В.; Толмачев, А. А.; Овчинников, А. С.

doi:10.15826/recon.2020.6.3.011

Cited by 3 publications

(11 citation statements)

References 9 publications

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“…New statistical data on the course of the disease caused by the SARS-CoV-2 virus, that appeared since the publication of our previous work, led to some transformations of the graph in Fig. 1, in comparison with that one in [1]:…”

Section: Theoretical Foundations Of the Analysis Of The Covid-19 Spreadmentioning

confidence: 98%

“…As in the previous work of the authors [1] for modeling the COVID-19 epidemic, we divide the population into the following clusters (groups): I -infected individuals in whom the disease has not been identified: those who are ill without clinical symptoms, who did not apply to medical institutions or were mistakenly diagnosed with the absence of the disease. H -healthy and susceptible to disease individuals.…”

Section: Theoretical Foundations Of the Analysis Of The Covid-19 Spreadmentioning

confidence: 99%

“…These values were defined in the paper [1]: = 0,12; = 65.3% To evaluate the probability in (4,5) we also use the model of the work [11], in which:…”

Section: Theoretical Foundations Of the Analysis Of The Covid-19 Spreadmentioning

confidence: 99%

“…This study is the second work of the authors devoted to this problem. In a previous work [1] we presented a review of works (see, for example [2][3][4][5][6][7][8][9]) devoted to modeling of the epidemics. In particular, the last of the mentioned works laid the foundations of the so-called SIR epidemics models (from the English Susceptible -Infected -Recovered).…”

Section: Introductionmentioning

confidence: 99%

“…In connection with the above, the main goals of our present research are: ⎯ The adaptation of a probabilistic mathematical model [1] describing the course of the epidemic to its current phase.…”

Section: Introductionmentioning

confidence: 99%

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Model of socio-economic factors in the spread of SARS-CoV-2 across Russian regions

2021

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The worldwide spread of a new infection SARS-CoV-2 makes relevant the analysis of the different factors that lead to the vulnerability of modern civilization to previously unknown diseases. In this regard, the development of mathematical models describing the evolution of epidemics like COVID-19 and the identification of socio-economic factors affecting the epidemiological situation in regions is an important research task. The paper proposes a probabilistic mathematical model for the spread of the COVID-19 epidemic, which allows to analyze the evolution of the main characteristics of the disease and to assess main factors influencing them. The study is based on the official statistical data on the spread of the COVID 19 presented on coronavirus sites in the Russian Federation and other countries, the Yandex Data Lens dataset service, as well as the data from the Federal State Statistics Service. In the research some data mining methods were used for evaluation the model’s parameters. The model equations allow to predict the evolution of the disease and estimate the confidence interval of such prognosis. We estimated the ratio of detected and hidden cases of the disease, the distribution of the disease’s duration probability and its average value for different regions. It has been mathematically proven that the vaccination is the necessary and sufficient condition of achievement a stationary stable state - the cessation of a pandemic. The regions of Russian Federation were clustered by the course of the disease COVID-19 on the base of k-means method. The analysis of the most important socio-economic factors affecting the epidemiological situation was provided separately for each cluster.

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Section: Theoretical Foundations Of the Analysis Of The Covid-19 Spreadmentioning

confidence: 98%

Section: Theoretical Foundations Of the Analysis Of The Covid-19 Spreadmentioning

confidence: 99%

“…These values were defined in the paper [1]: = 0,12; = 65.3% To evaluate the probability in (4,5) we also use the model of the work [11], in which:…”

Section: Theoretical Foundations Of the Analysis Of The Covid-19 Spreadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model of socio-economic factors in the spread of SARS-CoV-2 across Russian regions

2021

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New trends and competition in the market of higher education services in the context of digitalization

et al. 2021

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The aim of the study was to analyze new trends, competition and structural changes in higher education in the context of digitalization. The main directions of higher education development in the XXI century are identified. It is shown that by 2020, new trends in the market of higher education services have appeared and fully manifested themselves, due to three key factors: 1) increased global competition; 2) the active introduction of digital technologies in higher education; 3) the impact of the global COVID pandemic on educational technologies. It is shown that the coronavirus pandemic was a powerful incentive for the transition to digital technologies in higher education. Thanks to digitalization, the structure of the market has changed, and the number of players has increased significantly, and the market consists of private and public institutions, ministries of education and government agencies, educational, consulting and test (rating) companies, etc. Moreover, the global competition of universities for quality is becoming more organized and technically equipped; it increasingly relies on impersonal methods of assessing quality using digital technologies. At the same time, the authors draw a number of conclusions about the negative consequences of distance education. It is shown that competitiveness and accessibility of educational services for different categories of citizens in the difficult conditions of global challenges and threats, the wave pandemic, is ensured through high-quality interaction of all participants in the process, the activity of scientists and practitioners around the world, and state support for teaching initiatives in all regions of the country.

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Analysis of the readiness of Russian regions for the COVID-19 pandemic based on demographic sustainability and development of the healthcare system

Rudneva

Sokolov

2023

Population

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Assessment of the readiness of regions for the occurrence of critical situations is atopical area of research in modern conditions. The problem of high mortality in Russia determines the importance of studying the factors that shape the current state of the socio-demographic system of the population and the prerequisites for its change in the future or in the present under the influence of negative processes. To form an objective picture of the current situation, two areas are identified: potential (the state of the region's healthcare system) and demographic stability (risks of population decline), on the basis of which complex integral indices are calculated and regions are ranked. The specificity of the author's approach to the analysis consists in the complex use of demographic indicators, morbidity indicators and the functioning of the healthcare system. As a result of the analysis, it was found out that an ambiguous situation has developed in the regions. According to the assessment of demographic sustainability, the republics of the North Caucasus have become the best, but there is also the lowest level of development of the healthcare system. The opposite situation is observed in remote and sparsely populated regions of the Far East — Sakhalin Oblast, the Jewish Autonomous Oblast and Chukotka Autonomous Okrug — with high indicators of the potential of the healthcare system there are high demographic risks. The depth of the consequences of the pandemic was reflected in the form of excess mortality. In the first year, the regions of the North Caucasus became leaders in terms of mortality growth — Chechnya (by 44%), Dagestan (by 32%) and Ingushetia (by 27%).The most stable were Adygea, Sevastopol and Buryatia — excess mortality did not exceed 6%. In the second year, actively increasing mortality has already affected all regions, with the exception of the Republic of Tyva and Chukotka Autonomous Okrug having the lowest proportion of the population over working age. The proposed concept of a two-way study of regions will make it possible to trace the main problem areas in dynamics and give an opportunity to prevent the consequences of other possible force majeure.

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Socio-economic factors in the spread of SARS-COV-2 across Russian regions

Cited by 3 publications

References 9 publications

Model of socio-economic factors in the spread of SARS-CoV-2 across Russian regions

Model of socio-economic factors in the spread of SARS-CoV-2 across Russian regions

New trends and competition in the market of higher education services in the context of digitalization

Analysis of the readiness of Russian regions for the COVID-19 pandemic based on demographic sustainability and development of the healthcare system

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