Global-scale analysis and longitudinal assessment of COVID-19 incidence in the first six months

Studies examining factors responsible for COVID-19 incidence have mostly focused at the national or sub-national level. Here we undertake an analysis of COVID-19 cases at the global scale to identify key factors associated with disease incidence. A regression modeling framework was used to identify key variables associated with COVID-19 incidence, and to assess longitudinal trends in reported incidence at the country-level. New COVID-19 case dynamics in response to lockdowns was characterized via cluster analysis. Eleven variables were found to be independently associated with COVID-19 infections (p<1e-05) and a 4-variable model adequately explained global variations in COVID-19 cases (p<0.01). COVID-19 case trajectories for most countries followed the log-logistic curve. Six predominant country clusters summarized the differences in individual country’s response to lockdowns. Globally, economic and meteorological factors are important determinants of COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 caseloads further highlights important nuances in country-specific responses to the pandemic. These findings on the first six months of the pandemic has important implications for additional phases of the disease currently underway in many countries.

The Y chromosome ancestry marker R1b1b2: a surrogate of the SARS-CoV-2 population affinity

Individual and population susceptibilities to disease remain a murky area of investigation, clouded by past bias based on ideological differences and wars. The current SARS-CoV-2 pandemic, the largest in living memory, brought this matter to forefront as the disparity in disease burden became apparent. A timeline analysis of the pandemic revealed the presence of country clusters that display a marked preponderance of disease among populations carrying the ancestry marker R1b1b2, notably associated with both infection and mortality. This marker is a relic of past human expansions from western Asia and subsequently Europe and the rest of the world, which may have been accompanied by peculiar biological events rendering these populations vulnerable to SARS-CoV-2.

Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the “music mainstream” strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user’s country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.

Teaching and learning in a multilingual Europe: findings from a cross-European study

School classrooms within the EU are multilingual learning environments. The diversity of pupils in classrooms raises significant challenges for teachers, but to date, there are no data from large-scale surveys that compare views within and across European countries. A bespoke questionnaire was designed to examine views of current classroom learning environments with respect to the multilingualism. The questionnaire was piloted and subsequently completed by 2792 teachers across different European countries. Eleven countries provided sufficient data for analyses. Results from structural equation modelling showed that teachers’ attitudes could be reliably measured across Europe with the use of carefully devised questionnaire, whose loading and factor structure remained invariant across countries. Teachers’ views about multilingualism were most challenged by the numbers of children in their classes, not the percentage of multilingual pupils in the class. Countries differed in how they perceived multilingualism, with their differences leading to distinctive country clusters. Gender and education level (elementary vs. secondary) differences were also observed irrespective of country. These findings enhance our understanding of the role that the characteristics of teachers and their classrooms play in a multilingual setting across diverse European settings. The practical relevance of the results and new opportunities for teacher training are discussed.

Mortality, Severity, and Hospital Admission among COVID-19 Patients with ACEI/ARB Use: A Meta-Analysis Stratifying Countries Based on Response to the First Wave of the Pandemic

Background: The use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) is controversial for treating COVID-19 patients. We aimed to estimate pooled risks of mortality, disease severity, and hospitalization associated with ACEI/ARB use and stratify them by country and country clusters. Methods: We conducted a search in various databases through 4 July 2020 and then applied random-effects models to estimate pooled risks (ORp) across stratifications by country cluster. Clusters were chosen to reflect outbreak times (China followed by Korea/Italy, others subsequently) and mobility restrictions (China and Denmark/France/Spain with stricter lockdowns than the UK/US). Results: Overall analysis showed no increase in mortality; however, a statistical increase in mortality was seen in the US/UK cluster with ORp = 1.28 [95% CI = 1.04; 1.56] and a decrease in China with ORp = 0.65 [95% CI = 0.43; 0.96] and France with OR = 0.31 [95% CI = 0.14; 0.69]. Severity and hospitalization were not statistically significant in the analysis; however, several associations were seen in specific countries but not in country clusters. Conclusion: The country-cluster meta-analysis provided a reasonable explanation for COVID-19 mortality among ACEI/ARB users. The analysis did not explain differences in severity and suggested the involvement of other factors. Hospitalization findings among ACEI/ARB users may be considered informative as they may have been subjected to clinical decisions and hospital-bed availability.

Closed-Loop Supply Chain: A bibliometric and visualization analysis

Nowadays, enterprises have to be good for society, to take care of the environment, and to achieve profit at the same time. And the closed-loop supply chain helps them being so. However, there is a lack of bibliometric and visualization research in the area of “Closed-Loop Supply Chain”. Thus, this research aims to present a bibliometric overview to define the current state of scientific production regarding “Closed-Loop Supply Chain”. The review of 807 publications from the Scopus database (1995–2020) was conducted. Two combinations of words with the logical operator (“supply chain” AND “reverse logistics”) were used. The “title, abstract, keywords” field of search in the Scopus database was done. The visualization of the results was made using VOSviewer program to graphically map the material. The study used the co-occurrence of keywords and co-authorship (country) analyses. As a result, the most productive authors and journals were defined. The most cited studies were determined. Country clusters and keywords (co-occurrence) clusters were represented. The obtained results of the analysis and graphical presentations are relevant, and they form the basis for a better understanding of the concept of Closed-Loop Supply Chain.

A systematic literature review of smart grids

The development and implementation of smart grids involve developing new and improvements in existing energy technologies, introducing information systems to manage the smart grid, monitoring and controlling energy consumption, and closely related to alternative energy and decarbonization of the economy. Scientific research of smart grids differs significantly in terms of topics because they aim to solve problems in each of these areas. Thus, this research aims to present a bibliometric overview to define the current scientific production state regarding “Smart Grid.” A review of 1359 publications from the Scopus database (2008–2020) was conducted. The “Title, abstract, keywords” field of search in the Scopus database was done. The visualization of the results was made using VOSviewer program to map the material graphically. The study used the cooccurrence of keywords and co-authorship (country) analyzes. As a result, the most productive authors and journals were defined. The most cited studies were determined. Country clusters and keywords (co-occurrence) clusters were represented. The obtained results of the analysis and graphical presentations are relevant, and they form the basis for a better understanding of Smart Grid’s concept.

Cross-country clustering of labor and education markets in the system of strategic economic

The study focuses on the clustering of European countries in terms of labor market and education indicators, as well as the justification of strategic directions for ensuring GDP growth. It is proved that the management of the national economy in modern conditions acquires supranational features and can be effective, provided that the same approaches to the strategic management of facilities that have the same characteristics (belonging to the same cluster) are used. A comparative analysis of the development trends of labor markets of the EU and Ukraine is carried out. Using the K-means method, three cross-country clusters were formed according to indicators of the labor market and education. The applied use of the formed clusters of European countries according to indicators of the labor market and education is that for each cross-country cluster strategic directions of economic development can be justified by regulating the situation on the labor market and in the field of education. On the example of Slovakia, Austria, Greece and Ukraine, models that describe the impact on GGP of labor market and education indicators for countries are constructed. As a result of the study, strategic directions for each cluster are substantiated, which should ensure economic growth.

Comparative prevalence of COVID–19 in european countries: a time window at second wave

Introduction: The pandemic generated by COVID–19 completely changed people's daily lives, their relationship with family and friends, unexpectedly disrupted their working conditions and enhanced the need for an enduring resilience to face yet a second wave of the disease. It is crucial to keep continuously updating our knowledge about COVID–19 prevalence and incidence evolutions over large connected territories, where the disease is striking in alarming proportions. Objective: The main objective of this research is to identify and describe COVID–19 prevalence, incidence and mortality profiles in EU and EEE/EFTA countries, seven months after the start of the pandemic in Europe, and more recent tendencies, probably associated to the beginning of a second wave. Methods: This COVID–19 study covers thirty–one European countries. Six epidemiological variables where analyzed per 100 000 inhabitants on October 25 2020, two of them evaluated over the seven previous days. A multivariate statistical exploratory analysis based on rank principal components and cluster analysis was applied. Results: A COVID–19 prevalence typology of six country clusters was identified regarding 31 countries (EU, UK and three EEE/EFTA countries). The five epidemiological variables and number of tests revealed a wider dispersion with outlier observations. The rank transformation of data and their multivariate statistical analysis allowed us to construct a rational to better discriminate and describe these clusters, identifying specific behaviours related to the global prevalence from March until the end of October or highlight recent evolutions of COVID–19 incidence in the context of a second wave of pandemic. In fact we pinpointed country clusters where COVID–19 reached alarming levels which persist, or have even worsen, at the beginning of the second wave. Additionally, two other clusters were identified: one with countries that seems to be evolving into a situation under control, and another cluster of countries very weakly struck on the first wave, but are now facing a very complex surge, that will test their health systems capacity and timely response regarding covid and non–covid patients. Finally, the worst and more dramatic situation occurred in countries where the number of deaths per 100 000 inhabitants attained an impressive cumulative score.