ABSTRACT
Background. The ongoing pandemic of a new coronavirus infection (COVID-19) determines the relevance of the analysis of epidemiological patterns of SARS-CoV-2 spread among the population of the Russian Federation. Aim — study of the manifestations of the epidemic process of COVID-19 in the Russian Federation in 2020–2022. Materials and methods. A retrospective epidemiological analysis of the incidence of COVID-19 in the Russian Federation was carried out from 03/30/2020 to 04/24/2022. The data from the Rospotrebnadzor report No. 970 “Information on cases of infectious diseases in persons with suspected new coronavirus infection”, information portal Stopcoronavirus.rf, etc. were used. The presence of SARS-CoV-2 RNA was confirmed by real-time RT-PCR. Results and discussion. The analysis of the manifestations of the epidemic process of COVID-19 in the Russian Federation in 2020–2022 showed the presence of two stages which differed depending on the influence of the biological factor and the ongoing anti-epidemic measures. There was a pronounced trend in the development of the epidemic process, starting from megacities (Moscow, Moscow region and St. Petersburg), which are major transport hubs and centers of migration activity of the population, to the regions of the Russian Federation. The SARS-CoV-2 pathogenicity has been shown to decrease with each subsequent cycle of the rise in the incidence of COVID-19 against the background of the increased contagiousness of the virus. Conclusion. As a result of the study, risk areas (megacities) and risk groups were identified. © 2022, Central Research Institute for Epidemiology. All rights reserved.
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.
ABSTRACT
In this article, we analyzed the problems associated with increasing antibiotic resistance, irrational use of antibiotics, and inadequate demand for them during the COVID-19 pandemic. Objective. Using the method of digital epidemiology, we analyzed the dynamics of the frequency of a specific request for antibiotics in pharmacies and hospitals. We used open data from Yandex (Wordstat.Yandex) and Google (Google Trends) collected on weekly basis for the Russian Federation. Results. The World Health Organization reports a growing problem of antibiotic misuse by some individuals and healthcare institutions during the COVID-19 pandemic. Extensive irrational use of antibiotics causes the development of antibiotic resistance by many microorganisms, including those circulating in hospitals (for example, ESKAPE group). Moreover, COVID-19 has led to an exponential increase in the use of biocides worldwide, potentially resulting in additional indirect pressure promoting the selection of antibiotic-resistant strains. The pandemic in Russia was marked by a significant increase in antibiotic sales in pharmacies (including systemic antibacterial agents) and purchases by healthcare institutions. Conclusion. Our findings demonstrate that the rapid spread of COVID-19 was associated with extensive consumption of antibiotics, which resulted in growing antibacterial resistance (number of circulating drug-resistant strains) and posed a threat to the national security. The COVID-19 necessitates the discovery of new effective treatments for this infection, as well as rational use of antimicrobial drugs. The implementation of surveillance of antibiotic consumption will help to identify changing trends in their use, combine efforts to solve problems related to antibiotics and drug resistance, and to ensure rational use of antimicrobials. © 2021, Dynasty Publishing House. All rights reserved.