How the pandemic affected interregional inequality in Russia

Analysis of trends in interregional inequality in Russia in 2015–21 and of the actual outcome during the 2020 pandemic and the subsequent recovery in 2021 reveals short-term regional convergence in seven indicators, albeit of different depth and duration. Sub-federal budget revenue experienced the most significant and persistent reduction in interregional disparities, the main sources of which were a reduction of unevenness in a number of taxes, a significant increase in federal transfers and a change in their nature. After a strong short-term convergence, industry, trade, transport and investment all tended to return to long-term divergence paths. Personal income and wage inequality responded weakly to the shock in the short term and entered the new long-term path. Multidirectional spatial trends resulted from the interaction of sectorial and fiscal policy effects during the pandemic. (English) [ FROM AUTHOR] Cómo ha afectado la pandemia en las desigualdades interregionales en Rusia. Area Development and Policy. En los análisis sobre las tendencias en las desigualdades interregional en Rusia durante el periodo de 2015 a 2021, el resultado actual durante la pandemia de 2020 y la recuperación posterior en 2021 se observa una convergencia regional a corto plazo en siete indicadores, si bien con diferencias en cuanto a la profundidad y la duración. En los ingresos presupuestales subfederales se observó la reducción más significativa y persistente en las desigualdades interregionales, siendo las principales fuentes la reducción de las desigualdades en una serie de impuestos, un aumento significativo en las transferencias federales y un cambio en su naturaleza. Tras una fuerte convergencia a corto plazo, la industria, el comercio, el transporte y las inversiones tendían a volver a las rutas de divergencia a largo plazo. Los ingresos personales y las desigualdades salariales respondieron débilmente al choque a corto plazo y entraron en una nueva fase a largo plazo. Las tendencias espaciales multidireccionales surgieron a partir de la interacción de los efectos de la política sectorial y fiscal durante la pandemia. (Spanish) [ FROM AUTHOR] Как пандемия повлияла на межрегиональное неравенство в России. Area Development and Policy. Анализ тенденций межрегионального неравенства в России в 2015–21 гг. и фактического неравенства во время пандемии 2020 г. и последующего восстановления в 2021 г. выявил краткосрочную конвергенцию регионов по семи показателям разной глубины и продолжительности. В доходах субфедерального бюджета произошло наиболее значительное и стойкое сокращение межрегиональных диспропорций, основными источниками которого стали уменьшение неравномерности по ряду налогов, значительное увеличение федеральных трансфертов и изменение их характера. После сильной краткосрочной конвергенции промышленность, торговля, транспорт и инвестиции, как правило, возвращались к долгосрочным траекториям дивергенции. Неравенство личных доходов и заработной платы слабо отреагировало на шок в краткосрочной перспективе и вступило на новую долгосрочную траекторию. Разнонаправленные пространственные тренды возникали в результате взаимодействия отраслевых эффектов и фискальной политики во время пандемии. (Russian) [ FROM AUTHOR] 流行病如何影响俄罗斯地区间不平等? Area Development and Policy. 本文分析了2015–21年间俄罗斯区域间不平等趋势、2020年爆发的流行病所造成的实际结果以及2021随后的疫情恢复情况, 研究表明七个指标在短期内区域趋同, 尽管深度和持续时间不同。联邦以下各级预算收入在区域间差异方面经历了最显著和持久的减少, 其主要来源是一些税收不平衡性减少, 以及联邦转移的显著增加和其性质的改变。在短期强劲趋同之后, 工业、贸易、运输和投资都倾向于回归长期的趋同路径。个人收入和工资不平等在短期内对冲击反应微弱, 并进入新的长期路径。多方的空间趋势是流行病期间部门和财政政策影响相互作用的结果。 (Chinese) [ FROM AUTHOR] Copyright of Area Development & Policy is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

RISE IN INCOME INEQUALITY BEFORE AND AFTER THE COVID-19 CRISIS IN ARGENTINA

This article examines inequality in income distribution in Argentina between 2014 and 2020 in a context of stagnation and economic crisis, which coincided with the outbreak of the Covid-19 pandemic. The determining factors of income distribution were analyzed based on a household survey, and a breakdown of the Gini coefficient was implemented to determine the factors that explained the increase in inequality. From a structuralist point of view, the retraction of formal employment, the expansion of the informal sector, and greater coverage of social protection policies were the central factors that explained the increased level of inequality at that time. Social transfers helped to mitigate inequality in the face of the Covid-19 pandemic. © The Author(s) 2022.

Realising the Sustainable Development Goal 11.7 in the post-pandemic era – A case study of Taiwan

The COVID-19 pandemic has dramatically impacted our daily lives worldwide. For instance, pandemic-prevention policies restrict people's mobility, which causes problems in accessing urban greenspaces. Indeed, unequal access to urban greenspace has been accentuated during the most stringent lockdowns of 2020 and 2021. Amid such challenging circumstances, there has been a growing attention placed on Sustainable Development Goal (SDG) 11.7, which has brought opportunities for urgent action. In this paper, we applied the Gini coefficient to our analysis of unequal access to urban greenspaces across all urban planning areas in six special municipalities in Taiwan. Moreover, we also conducted comparative analyses between the Gini coefficient and other socio-economic factors. The results show that approximately 63.98% of the urban planning area suffers from unequal access to greenspaces. In addition, urban greenspace provision and household income show significant positive correlations with the Gini coefficient, which reflects Taiwan's environmental injustice. Furthermore, these findings can help city planners and decision-makers evaluate levels of equality in each urban planning area and decide which priority areas should be improved. Finally, this study can also be used as a reference for decision-makers to realise SDG 11.7 in the post-pandemic era. © The Author(s) 2022.

Diagnosis of Skin Cancer by Using Fuzzy-Ann Expert System with Unification of Improved Gini Index Random Forest-Based Feature

In addition to being one of the most widespread and lethal diseases in the world, skin cancer is also one of the most common types of cancer. However, due to its complexity and fuzzy nature, the clinical diagnosis process of any disease, including skin cancer, prostate cancer, coronary artery disorders, diabetes, and COVID-19, is frequently accompanied by doubt. In order to address the uncertainty and ambiguity surrounding the diagnosis of skin cancer as well as the heavier burden on the overlay of the network nodes of the fuzzy neural network system that frequently occurs due to insignificant features that are used to predict or diagnose the disease, a fuzzy neural network expert system with an improved Gini index random forest-based feature importance measure algorithm was proposed in this work. A Greater Gini Index Out of the 30 features in the dataset, the five most fitting features of the diagnostic Wisconsin breast cancer database were chosen using a random forest-based feature importance measure algorithm. Two sets of classification models were created using the logistic regression, support vector machine, k-nearest neighbour, random forest, and Gaussian naive Bayes learning algorithms. As a result, models for classification that included all features (30) and models that only used the top five features were used. The efficacy of the two sets of categorization models was assessed, and the results of the assessment were compared. The comparison's results show that the models with the fittest features outperformed those with the most complete features in terms of accuracy, sensitivity, and sensitivity. A fuzzy neural network-based expert system was therefore developed, utilising the five best features, and it achieved 99.83 percent accuracy, 99.86 percent sensitivity, and 99.64 percent specificity. The system built in this study also stands to be the best in terms of accuracy, sensitivity, and specificity when compared to prior research that used fuzzy neural networks or other applicable artificial intelligence techniques on the same dataset for the diagnosis of skin cancer. The z-test was also performed, and the test result demonstrates that the system has significantly improved accuracy for early skin cancer diagnosis. Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.

Rise in income inequality before and after the Covid-19 crisis in Argentina

This article examines inequality in income distribution in Argentina between 2014 and 2020 in a context of stagnation and economic crisis, which coincided with the outbreak of the Covid-19 pandemic. The determining factors of income distribution were analyzed based on a household survey, and a breakdown of the Gini coefficient was implemented to determine the factors that explained the increase in inequality. From a structuralist point of view, the retraction of formal employment, the expansion of the informal sector, and greater coverage of social protection policies were the central factors that explained the increased level of inequality at that time. Social transfers helped to mitigate inequality in the face of the Covid-19 pandemic. © 2023 Universidad Nacional Autonoma de Mexico. All rights reserved.

Rise in income inequality before and after the Covid-19 crisis in Argentina

This article examines inequality in income distribution in Argentina between 2014 and 2020 in a context of stagnation and economic crisis, which coincided with the outbreak of the Covid-19 pandemic. The determining factors of income distribution were analyzed based on a household survey, and a breakdown of the Gini coefficient was implemented to determine the factors that explained the increase in inequality. From a structuralist point of view, the retraction of formal employment, the expansion of the informal sector, and greater coverage of social protection policies were the central factors that explained the increased level of inequality at that time. Social transfers helped to mitigate inequality in the face of the Covid-19 pandemic. (English) [ FROM AUTHOR]

Lessons from COVID-19 pandemic: Analysis of unequal access to food stores using the Gini coefficient.

COVID-19 has dramatically altered daily life worldwide, with some urban residents resorting to panic buying at the beginning of the pandemic. Large-scale lockdowns and restaurant closures have increased the need for grocery shopping. Such shifts in consumer patterns have altered supply-demand systems. Insufficient food store availability increases the likelihood of crowding and thus increases the probability of viral infection. People who live without easy access to food stores also face high infection risks when forced to travel long distances for grocery shopping. The COVID-19 pandemic has demonstrated the importance of the number and distribution of food stores to virus transmission. Food access is also a core factor of urban resilience during the pandemic. This study used the Gini coefficient to investigate the fairness of accessibility to food stores at the city and village levels, with Taipei City chosen as the research area. Different spatial scales were considered, and we calculated the equality of food access for older (≥65 years old) and non-older populations separately to determine whether one group faces greater inequality. At the city level, both older and non-older populations in Taipei have reasonable access (Gini coefficient between 0.3 and 0.4), with mean Gini coefficients of 0.3616 and 0.3655, respectively. This city-level analysis represents the overall degree of unequal access to food stores. At the village level, eight villages (1.8 %; total N = 456) had severe access inequality (Gini coefficient > 0.6) for older adults; they are located primarily in downtown or suburban areas. For the non-older population, only two villages (0.4 %; total N = 456) in suburban areas exhibit severe access inequality. The village-level analysis identified villages with low equality of access to food stores and revealed local problems that cannot be observed at the city level.

COVID-19 SHOCKS: CROSS-COUNTRY ANALYSIS

The COVID-19 pandemic that engulfed the world in early 2020 did not make an exception for anyone. However, the degree of its impact on countries and social groups varies significantly. This article discusses several factors that can shape these differences, focusing specifically on the correlation between the excess mortality during 2020–2021 and various dimensions of social vulnerability, specifically the ones that are caused by unprotected or low pay employment, self-employment, and higher exposure to work-related social contacts. In order to measure these factors, this study relies on the analysis of the Gini coefficient and some other indices that reflect the specifics of labour markets and social vulnerabilities. The assessment is based on data provided by Eurofound, Eurobarometer, the World Bank and OECD Statistics. The correlation between inequality/vulnerability and excess mortality rates can be explained by higher exposure of vulnerable social groups to the infection and their lower access to high quality healthcare. The econometric analysis supports the hypothesis that the cross-country variation in excess mortality rates during the 2020–2021 COVID-19 pandemics can be partially explained by socioeconomic characteristics of countries in the sample. Vulnerable groups are not only more exposed to health-related risks due to the pandemics but are also less likely to be vaccinated. Larger shares of the vulnerable groups and lower vaccination rates are associated with higher excess mortality rates conditional upon country characteristics such as GDP per capita, the share of university graduates and healthcare expenditures, as well as age structure. This relationship holds for different variables used, different country samples and data sets. © 2022, Russian Academy of Sciences. All rights reserved.

Asymptotic properties of duration-based VaR backtests

To increase the power of the VaR tests, it has been recently proposed to extend the duration-based test class with the geometric-VaR and Gini-coefficient-based tests. These tests, though exhibiting outstanding power properties, have not gained recognition in the industry. A potential reason is the absence of ready-To-use statistical distributions. To remedy this, we inquire into the limiting properties of these tests and derive relevant asymptotic distributions. We also provide a generalized geometric-VaR test and give its distribution. Through the Monte Carlo study, we show the accuracy of our asymptotic procedures in finite samples, and we find these procedures to be relevant for the current Basel standards. Our theoretical results are illustrated by the empirical study that includes data from the current COVID-19 crisis. © 2022 Walter de Gruyter GmbH, Berlin/Boston 2022.

Dünya genelinde COVID-19 pandemisi yaygınlığı ile ilişkili faktörlere yönelik bir ekolojik çalışma

Purpose: The aim is to evaluate the relationships between the number of cases, deaths and tests in countries in the COVID-19 outbreak and the countries' Gini coefficients, elderly population rates, distances to the equator and global health security indexes. Materials and methods: In this ecological study conducted in August 2020, the data reported on the Worldometers website on the prevalence of the COVID-19 outbreak were used. The relationship between COVID-19 related variables of countries and Gini coefficients, elderly population ratios, distance from the equator and global health security indexes were examined. Results: 215 countries were evaluated in the study. Qatar is the country with the highest number of cases per million;San Marino has the highest number of deaths per million and Monaco has the highest number of tests per million. As a result of the linear regression analysis, the Gini coefficients of the countries were associated with the total number of cases per million, the elderly population ratios were associated with the total number of deaths per million, and distance to the equator was associated with the total number of tests per million. As the Gini coefficients of the countries increase, the total number of cases per million (p=0.006);as the elderly population rates increase, deaths per million (p=0.005);as the distance from the equator increases, the number of tests per million (p=0.015) increases. Conclusion: As a result, as income inequality, elderly population and distance from the equator increase, the impact from the pandemic increases. Keywords: COVID-19, pandemic, Gini coefficient, global health security index.

COVID-19 beyond cases and treatment. An estimate of inequality in access to diagnosis in Bogotá, 2020

Introduction. The COVID-19 pandemic has exerted unprecedented pressure on health systems, revealing inequalities on a world scale. One of the concerns over this period has been the possible inequality of access for diagnostic tests related to the economic resources of the population. In this study, we analyze the results of tests for COVID-19 detection in Bogotá and their relationship with income levels. Methods. Ecological research with SIVIGILA reports was carried out between March 6 to July 1, 2020, for positive and negative COVID-19 test records. The statistical description of the quantitative and qualitative variables and bivariate analysis were performed. Additionally, the Gini coefficient was calculated based on the Lorenz Curve. Results. The study included 44,300 records. The tests were conducted mainly on men (51.4%) and from the highest strata 4, 5, and 6 (53.5%). Similarly, the Gini Coefficient showed inequality in access by comparing socioeconomic strata. Conclusions. The analysis show inequality in access to the diagnostic tests for SARS-CoV-2, with the highest strata having more access associated with greater purchasing power. © 2021 Pontificia Universidad Javeriana. All rights reserved.

Nanomedicine for the poor: a lost cause or an idea whose time has yet to come?

The most effective COVID-19 vaccines, to date, utilize nanotechnology to deliver immunostimulatory mRNA. However, their high cost equates to low affordability. Total nano-vaccine purchases per capita and their proportion within the total vaccine lots have increased directly with the GDP per capita of countries. While three out of four COVID-19 vaccines procured by wealthy countries by the end of 2020 were nano-vaccines, this amounted to only one in ten for middle-income countries and nil for the low-income countries. Meanwhile, economic gains of saving lives with nano-vaccines in USA translate to large costs in middle-/low-income countries. It is discussed how nanomedicine can contribute to shrinking this gap between rich and poor instead of becoming an exquisite technology for the privileged. Two basic routes are outlined: (1) the use of qualitative contextual analyses to endorse R&D that positively affects the sociocultural climate; (2) challenging the commercial, competitive realities wherein scientific innovation of the day operates.

Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2.

The current life-threatening and tenacious pandemic eruption of coronavirus disease in 2019 (COVID-19) has posed a significant global hazard concerning high mortality rate, economic meltdown, and everyday life distress. The rapid spread of COVID-19 demands countermeasures to combat this deadly virus. Currently, there are no drugs approved by the FDA to treat COVID-19. Therefore, discovering small molecule therapeutics for treating COVID-19 infection is essential. So far, only a few small molecule inhibitors are reported for coronaviruses. There is a need to expand the small chemical space of coronaviruses inhibitors by adding potent and selective scaffolds with anti-COVID activity. In this context, the huge antiviral chemical space already available can be analysed using cheminformatic and machine learning to unearth new scaffolds. We created three specific datasets called "antiviral dataset" (N = 38,428) "drug-like antiviral dataset" (N = 20,963) and "anticorona dataset" (N = 433) for this purpose. We analyzed the 433 molecules of "anticorona dataset" for their scaffold diversity, physicochemical distributions, principal component analysis, activity cliffs, R-group decomposition, and scaffold mapping. The scaffold diversity of the "anticorona dataset" in terms of Murcko scaffold analysis demonstrates a thorough representation of diverse chemical scaffolds. However, physicochemical descriptor analysis and principal component analysis demonstrated negligible drug-like features for the "anticorona dataset" molecules. The "antiviral dataset" and "drug-like antiviral dataset" showed low scaffold diversity as measured by the Gini coefficient. The hierarchical clustering of the "antiviral dataset" against the "anticorona dataset" demonstrated little molecular similarity. We generated a library of frequent fragments and polypharmacological ligands targeting various essential viral proteins such as main protease, helicase, papain-like protease, and replicase polyprotein 1ab. Further structural and chemical features of the "anticorona dataset" were compared with SARS-CoV-2 repurposed drugs, FDA-approved drugs, natural products, and drugs currently in clinical trials. Using machine learning tool DCA (DMax Chemistry Assistant), we converted the "anticorona dataset" into an elegant hypothesis with significant functional biological relevance. Machine learning analysis uncovered that FDA approved drugs, Tizanidine HCl, Cefazolin, Raltegravir, Azilsartan, Acalabrutinib, Luliconazole, Sitagliptin, Meloxicam (Mobic), Succinyl sulfathiazole, Fluconazole, and Pranlukast could be repurposed as effective drugs for COVID-19. Fragment-based scaffold analysis and R-group decomposition uncovered pyrrolidine and the indole molecular scaffolds as the potent fragments for designing and synthesizing the novel drug-like molecules for targeting SARS-CoV-2. This comprehensive and systematic assessment of small-molecule viral therapeutics' entire chemical space realised critical insights to potentially privileged scaffolds that could aid in enrichment and rapid discovery of efficacious antiviral drugs for COVID-19.

CO2 emissions inequality through the lens of developing countries.

There is increasing interest in CO2 emissions inequality between and within countries, and concerns about the impacts of COVID-19 on vulnerable groups. In this study, the CO2 emissions inequality based on the different consumption category data of disaggregated income groups in eight developing countries is analyzed with the application of input-output model. We further examine the effects of the COVID-19 outbreak on CO2 emissions inequality based on the hypothetical extraction method, and the results reveal that the outbreak has decreased the CO2 emissions inequality and emissions over time. However, the shared socioeconomic pathway scenario simulation results indicate that long-term CO2 emissions inequality will persist. Targeted poverty elimination measures improve the utility of the low- and lowest-income groups and reduce CO2 emissions inequality. Reducing the excessive consumption on the demand side as well as improving the energy efficiency and increasing the share of renewable energy in the energy consumption on the supply side will provide more informed options to achieve multiple desirable outcomes, such as poverty elimination and climate change mitigation.