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Purpose: This paper aims to undertake the efficiency analysis in the form of stochastic frontier to estimate a Cobb–Douglas production function by controlling for the heterogeneity across Russian firms by including firm size, ownership, age, innovation activity and market competition. Design/methodology/approach: During the peak period of Covid-19, certain firms witnessed either a decrease or increase in sales. Using this segregation of firms from World Bank's Covid-19 impact surveys follow-up to the Enterprise Survey for Russia, this study empirically investigates the determinants of technical efficiency of these firms focusing on the role of government assistance. Findings: The findings suggest that by segregating firms in terms of sales, different internal factors can enable in steering through pandemic situation besides just depending on external assistance. Originality/value: One of the few papers to analyse the impact of the pandemic on Russian firms by considering World Bank Covid Survey. [ABSTRACT FROM AUTHOR]

The paper endeavours to explore and analyse some critical issues in the oil and gas market that cropped up around the spread of COVID-19 and tries to identify the key drivers and triggers pertaining therewith. The spread of the first wave that began in March 2020 is crucial because of the global economic downturn that ensued due to lockdown and imposed restrictions coupled with a protracted oil price war that began between Saudi Arabia and Russia. The paper tries to address some key research questions to understand the triggers and drivers around the pandemic. These are: (1) whether the behaviour of OPEC or its key players around the pandemic could be considered uniquely different; (2) what could the triggers be for the increased volatilities that cropped up in both physical and financial markets during the pandemic; (3) what was really different about the oil market crisis around the pandemic that transformed it to an unprecedented storage crisis; (4) what really went wrong with the much-hyped U.S. shale boom during the pandemic that led to the bankruptcy of several oil and gas companies, followed by huge job losses. The paper relies on a structured review of relevant secondary literature to address these exploratory questions and builds upon a retrospective rumination on the world oil market from 1960 to 2020. This is complemented by an analysis of supporting data and evidence obtained from various sources. Considering the intertwining of oil and financial markets around the pandemic, the lessons and findings from the paper would not only be highly relevant for policymakers and stakeholders in the oil and gas sector but would be equally relevant for those in the financial markets. [ABSTRACT FROM AUTHOR]

The flow chart of GVMD-ELM-ARIMA [Display omitted] Establishing an accurate and efficient prediction model is of great significance for governments and other social organizations to formulate prevention and control policies and curb the explosive spread of the pandemic. To improve prediction accuracy of cumulative COVID-19 confirmed data, a new hybrid prediction model based on gradient-based optimizer variational mode decomposition (GVMD), extreme learning machine (ELM), and autoregressive integrated moving average (ARIMA), named GVMD-ELM-ARIMA, is proposed. To solve the problem of selecting the k value and the penalty factor α in variational mode decomposition (VMD), this paper proposes gradient-based optimizer variational mode decomposition (GVMD), which realizes the self-adaptive determination of k value and α value. Firstly, GVMD decomposes the cumulative COVID-19 confirmed data into some intrinsic mode functions (IMFs) and a residual component (IMFr). Secondly, IMFs are predicted by ELM. Then, IMFr is predicted by ARIMA. Finally, the final prediction results are obtained by reconstructing the prediction result of IMFs and IMFr. The cumulative COVID-19 confirmed data of the United States, India and Russia is used to verify its effectiveness. Taking the United States as an example, compared with the average MAPE, RMSE and MAE of the single model, the average MAPE of the hybrid model is reduced by 47.27%, the average RMSE is reduced by 44.50%, and the average MAE is reduced by 55.34%. Compared with GVMD-ELM-ELM, GVMD-ELM-ARIMA proposed in this paper reduces the MAPE by 60%, the RMSE by 56.85%, and the MAE by 61.61%. The experimental results show that GVMD-ELM-ARIMA has best prediction accuracy, and it provides a new method for predicting the cumulative COVID-19 confirmed data. [ABSTRACT FROM AUTHOR]

The consideration of infectious diseases from a mathematical point of view can reveal possible options for epidemic control and fighting the spread of infection. However, predicting and modeling the spread of a new, previously unexplored virus is still difficult. The present paper examines the possibility of using a new approach to predicting the statistical indicators of the epidemic of a new type of virus based on the example of COVID-19. The important result of the study is the description of the principle of dynamic balance of epidemiological processes, which has not been previously used by other researchers for epidemic modeling. The new approach is also based on solving the problem of predicting the future dynamics of precisely random values of model parameters, which is used for defining the future values of the total number of: cases (C); recovered and dead (R); and active cases (I). Intelligent heuristic algorithms are proposed for calculating the future trajectories of stochastic parameters, which are called the percentage increase in the total number of confirmed cases of the disease and the dynamic characteristics of epidemiological processes. Examples are given of the application of the proposed approach for making forecasts of the considered indicators of the COVID-19 epidemic, in Russia and European countries, during the first wave of the epidemic. [ABSTRACT FROM AUTHOR]

• Russia demonstrates low vaccination intentions compared to most European countries and some emerging economies. • The paper uses a nationally representative dataset for Russia (RLMS HSE-2020) to analyze the scope of COVID-19 vaccination hesitancy and resistance prior to the start of the vaccination campaign. • 43% of the Russian adult population were resistant towards vaccination, while 13% were hesitant. • Vaccination attitudes depend on age, family composition, education, type of settlement, employment, self-rated health, previous COVID-19 experience, and self-perceived risk of being infected. • Personality traits, risk attitudes, and trust, reflect the deep-rooted causes of vaccination attitudes and strongly predict vaccination intentions. Vaccination is the main tool available to handle the COVID-19 pandemic globally. Though no vaccine is proven to be 100% effective, vaccination secures against getting seriously ill and dying from the disease. Russia announced the development of its first domestic vaccine back in August 2020 and launched the nationwide immunization campaign at the beginning of 2021. Despite these achievements, as of mid-October 2021, only 36% of the population got at least one shot of the vaccine. Massive vaccination hesitancy and refusal pose a great threat to public health and postpone social and economic recovery. Using nationally representative data from the general adult population of Russia, this study explores the scope of vaccination hesitancy and refusal as well as the determinants of vaccination attitudes. The results suggest that only 45% of the Russian population demonstrated positive attitudes towards the COVID-19 vaccination prior to the launch of a nationwide vaccination program. We analyze a wide array of demographic, socio-economic, and health-related factors in relation to vaccination intentions and explore the deep-rooted causes of vaccination reluctance by looking at personality traits, religiosity, and trust. The obtained results are vital for designing policy measures to promote vaccination. [ABSTRACT FROM AUTHOR]

This paper dwells upon COVID-19-related efforts of the Center for Sports Medicine, Federal Medical and Biological Agency of Russia. The Agency has the following precautions in place: regular polymerase chain reaction (PCR) testing of athletes and staff; double PCR testing before going to training camps or medical examinations; isolating athletes and their traceable contacts when COVID is suspected; observation and isolation wards set up at training camp venues. Athlete vaccination has begun. Athletes are provided online advice on health, diet, and exercising plus special care for chronically ill athletes and remote psychological counseling. Athletes recovering from COVID-19 are offered rehabilitation programs and doctor-supervised return to training. Specialists of the Research Department at FMBA's Center for Sports Medicine carried out a research dedicated to the prevalence of COVID-19 and different variants of its course in Russian athletes. The study period lasted from March to December 2020. A total of 27,438 records were analyzed. In May, June, July and August 2020, the percentage of positive PCR tests for athletes was significantly lower than the nationwide percentage at p < 0.05, Pearson's chi-squared test. However, the differences were nullified by September–October. The disease was mild or asymptomatic in most patients. Athletes of summer sports were found to be most likely to contract COVID-19. [ABSTRACT FROM AUTHOR]

At this moment in time, an outbreak of COVID-19 is transmitting from human to human. Different parts have different quality of life (e.g., India compared to Russia), which implies the impact varies in each part of the world. Although clinical vaccines are available to cure, the question is how to minimize the spread without considering the vaccine. In this paper, via a mathematical model, the transmission dynamics of novel coronavirus with quarantine and isolation facilities have been proposed. The examination of the proposed model is set in motion with the boundedness and positivity of the solution, sole disease-free equilibrium, and local stability. Then, the condition for the existence of sole endemic equilibrium and its local stability has established. In addition, the global stability of the endemic equilibrium for a special case has been investigated. Further, it has shown that the system undergoes a transcritical bifurcation. A threshold analysis has also performed to examine the effect of quarantine on transmission dynamics. Lastly, numerical simulations are giving support to theoretical results. [ABSTRACT FROM AUTHOR]

The global economy appears the trend of anti-globalization under the influence of COVID-19. Based on the input-output table of lead database from 2006 to 2020, this paper divides the factors that affect the development of financial industry in China, the United States and Russia into six aspects: price, intermediate input, household consumption, government consumption, export and import. ADGA-BP neural network model is proposed in this paper, which is based on six aspects of price, intermediate input, consumer, government consumption, export and import. The intermediate input is decomposed from the perspective of industrial structure to study the interrelationship between financial industry and other industries in the three countries. The results show that the intermediate input is the main factor in the development of financial industry in the three countries, but the source industries of the intermediate input are not the same; the two factors of household consumption and price are closely related to the development of financial industry in the three countries, and they all play a role in promoting China, while the relationship between household consumption and the United States and between price and Russia is reverse; Government consumption only has a significant impact on Russia; from the perspective of mutual influence, the mutual investment between the financial industry of China and the United States is relatively large, while the relationship between the Russian financial industry and the two countries is relatively weak. It shows that under the background of covid-19, the development of financial industry is affected. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 pandemic has become a stress test for science journalism worldwide. In this paper, we explore the under-researched topic of the perceived role enactment of science journalists in non-democratic settings in the example of COVID-19 coverage in Russia. Drawing on 23 semi-structured interviews with Russian science journalists from pro-state and independent media outlets, we examine their perceived role enactment and politicization of reporting about the pandemic in Russia. We identify three major types of limitations of a political nature reported by journalists: (1) editorial guidelines; (2) accessibility and self-censorship of sources; (3) self-censorship. Manifestation of these three types of limitations differ depending on the political orientation of media outlets as well as on the aspect of the topic. Based on the findings, we discuss the influence of universal trends and contextual factors on science journalism covering the pandemic. [ABSTRACT FROM AUTHOR]

This paper seeks to examine the COVID-19 crisis in Russia, France, Germany, and the UK, as covered by the Russian state media outlet RT (formerly Russia Today). I view the RT coverage through the prism of biopolitics and critical discourse analysis (CDA) to demonstrate multiple discrepancies in its "post-truth" knowledge production strategies. I argue that these strategies aim to expose the hybrid and controversial nature of biopolitical governance in Western democracies during the COVID-19 pandemic as they struggle to strike a balance between imposing social restrictions and safeguarding public health. I also show how the (post)liberal biopolitical debate on personal responsibility and state resilience in times of emergency could be applied by authoritarian regimes for self-description. [ABSTRACT FROM AUTHOR]

Purpose: The outbreak of the coronavirus disease 2019 (COVID-19) pandemic is an unprecedented shock to the BRICS (Brazil, Russia, India, China, South Africa) economy and their financial markets have plummeted significantly due to it. This paper adds to the recent literature on contagion due to spillover by uniquely examining the presence of pairwise contagion or volatility transmissions in stock markets returns of India, Brazil, Russia, China and USA prior to and during COVID-19 pandemic period. Design/methodology/approach: In this study, the generalised autoregressive conditional heteroskedasticity (GARCH) by Bollerslev (1986) under diagonal parameterization is used to estimate multivariate GARCH framework also known as BEKK (Baba EngleKraft and Kroner) model on stock market returns of BRIC nations and the US. Findings: The empirical results show that the model captures the volatility spillovers and display statistical significance for own past mean and volatility with both short- and long-run persistence effects. Own volatility spillovers (Heatwave phenomenon) have been found to be highest for the US, China and Brazil compared to Russia and India. The coefficients indicate persistence of volatility for each country in terms of its own past errors. The highest and long-term spillover effect is found between US and Russia. The results recommend that Russia is least vulnerable to outside shocks. Finally after examining the pairwise results, it is suggested that the BRIC countries stock indices have exhibited volatility spillover due to the COVID-19 pandemic. Research limitations/implications: The study may be extended to include other emerging market economies under a dynamic framework. Practical implications: Researchers and policymakers may draw useful insights on cross-market interdependencies regarding the spillovers in BRIC countries' stock markets. It also helps design international portfolio diversification strategies and in constructing optimal portfolios during COVID and in a post-COVID world. Originality/value: COVID-19 has been an improbable event in the history of the world which can have a large impact on the financial economies across the emerging countries. This event can be deemed to be informative enough to measure the co-movements of the equity markets amongst cross-country return series, which has not been investigated so far for BRIC nations. [ABSTRACT FROM AUTHOR]

Introduction. The article presents information on the activities of inpatient facilities for infectious diseases during the pandemic of the new coronavirus disease 2019, taking into account current regulatory documents. The authors reviewed the principles of hospital zoning and patient controlled movement in hospitals for infectious diseases. The paper deals with the organization of the admission departments and equipment of the diagnostic units of hospitals for infectious diseases, providing a graphical presentation of an individual isolation unit (Melzer box) including description of the control and management system for safe access to infectious units. The characteristics of engineering and communication systems, disposal of hazardous medical waste, catering, navigation systems, operational remote communication between doctors and patients, application of barcoding for patient identification and medical records are also discussed. The purpose of this paper was to: Identify the shortcomings of the existing regulatory framework concerning the management and organization of health care institutions that provide medical care to patients with the new coronavirus infection; Identify areas that require adjustments, given the modern requirements for high quality treatment, as well as to ensure epidemiological safety for medical staff and patients; Specify additional requirements for hospitals for infectious diseases, which should be taken into account when planning major repairs, reconstruction and construction of new medical facilities for providing health care during the pandemic of new coronavirus disease 2019 and other highly infectious infections. [ABSTRACT FROM AUTHOR]

In 2020, the world is faced with COVID-19 pandemic as one of the worst global catastrophes of the 21st century. The governments of countries around the world have introduced various emergency rules on the territory of their states as a measure to contain the pandemic. Are such restrictions permissible and how justified were they? Did the restrictions imposed comply with the norms enshrined in international human rights standards? Have states managed to strike a balance between "legality" and "necessity"? We will try to answer all these questions in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we apply an algorithm for simulating the susceptible-infected-recovered-dead pandemic model for demonstrating the forecasting and the spreading of the new coronavirus disease. For this purpose, we apply an implicit analytical solution for parts of the model, in addition to applying the finite difference methods for other parts of the model. Based on the collected data of the number of infected cases, recovered cases, and deceased cases of the new coronavirus pandemic as of 30 May 2020, we find the values of the coefficient of infection, the coefficient of recovery, and the coefficient of mortality of the new coronavirus pandemic for four different countries, namely, China, the United States, Russia, and the Syrian Arab Republic. Besides, we find the ratio of the average rate of recovery to the average rate of death of the new coronavirus pandemic for the same four countries. For the following months, we predict the number of the infected cases, the recovered cases, and the deceased cases of the pandemic for the United States, Russia, and the Syrian Arab Republic using the methods normally used for the epidemic model. We find that the number of infected cases of the new coronavirus disease may increase to about two million cases in the United States, about eight hundred thousand cases in Russia, and about three hundred cases in the Syrian Arab Republic. We believe that the algorithm we use for simulating the spreading and the forecasting of the new coronavirus disease can be applied for finding other related coefficients of the pandemic such as the basic reproduction numbers with a certain population. [ABSTRACT FROM AUTHOR]

Tuberculosis is still an important medical and social problem. In recent years, great strides have been made in the fight against M. tuberculosis, especially in the Russian Federation. However, the emergence of a new coronavirus infection (COVID-19) has led to the long-term isolation of the population on the one hand and to the relevance of using personal protective equipment on the other. Our knowledge regarding SARS-CoV-2-induced inflammation and tissue destruction is rapidly expanding, while our understanding of the pathology of human pulmonary tuberculosis gained through more the 100 years of research is still limited. This paper reviews the main molecular and cellular differences and similarities caused by M. tuberculosis and SARS-CoV-2 infections, as well as their critical immunological and pathomorphological features. Immune suppression caused by the SARS-CoV-2 virus may result in certain difficulties in the diagnosis and treatment of tuberculosis. Furthermore, long-term lymphopenia, hyperinflammation, lung tissue injury and imbalance in CD4+ T cell subsets associated with COVID-19 could propagate M. tuberculosis infection and disease progression. [ABSTRACT FROM AUTHOR]

The Russian government took strong containment measures to prevent the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rigid hygiene protocols and restrictions on daily living, such as social distancing and closing businesses and schools. While these measures were crucial to stop the diffusion of SARS-CoV-2, numerous voices highlighted their disorderly psychological, social, and economic impacts on food consumption behavior and lifestyle. Therefore, this paper aims to understand how consumers' food-related habits in Russia have shifted due to the COVID-19 pandemic and potential problems and opportunities this might bring for the Russian food system. The study is based on an online cross-sectional survey using a structured questionnaire administered in Russian through the Survey Monkey platform. A total of 1297 valid answers was collected. The results reveal that diet and food shopping, preparation, and use have been widely affected. Indeed, the survey results suggested (i) a change in the modality of acquiring food, consumers reduced the number of shopping trips and buying more on each trip to minimize store visits; (ii) a surge of stockpiling of non-perishable food items; (iii) a shift toward healthier diets; (iv) an increase in culinary capabilities; (v) a decrease of food waste. The results are expected to inform current emergency plans as well as long-term food-related strategies in Russia. [ABSTRACT FROM AUTHOR]

Like most countries globally, COVID-19 continues to have a demonstrable health, economic, and environmental impact on Russia. The purpose of this paper is to examine the possible ramifications for environmental quality in Russia during and following the coronavirus pandemic. Our work builds on the framework of Elinor Ostrom, as we argue that the pandemic and subsequent lockdown in Russia has highlighted the need for a more polycentric, de-centralized approach to environmental protection. We provide evidence for this point using a novel econometric strategy: given the tight centralization of environmental policymaking, we proxy for de facto decentralization using the amount of influence a regional governor has at the federal level. Using timely data on pollution in major Russian cities both before and during the pandemic, we employ an instrumental variable analysis which shows that pollution in a particular Russian region is negatively related to the amount of influence a Russian governor has at the federal level. Thus, the more powerful a governor is in their ability to set their own course, the better results they have in environmental quality. We conclude that Russia's environmental policy needs a fundamental rethink – and extensive decentralization – in a post-COVID-19 world. • We study the impact of the COVID-19 pandemic on environmental quality in Russia. • Building on Elinor Ostrom's framework, we highlight the need for a polycentric environmental policy. • Timely and novel data on pollution, regional governors' influence, and self-isolation are used. • The regional governor's influence at the federal level proxies for de facto decentralization. • An instrumental variable analysis shows that pollution is negatively related to the governor's influence. [ABSTRACT FROM AUTHOR]

Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices. [ABSTRACT FROM AUTHOR]