Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: evidence from Bayesian model averaging.

The COVID-19 pandemic resulted in great discrepancies in both infection and mortality rates between countries. Besides the biological and epidemiological factors, a multitude of social and economic criteria also influenced the extent to which these discrepancies appeared. Consequently, there is an active debate regarding the critical socio-economic and health factors that correlate with the infection and mortality rates outcome of the pandemic. Here, we leverage Bayesian model averaging techniques and country level data to investigate whether 28 variables, which describe a diverse set of health and socio-economic characteristics, correlate with the final number of infections and deaths during the first wave of the coronavirus pandemic. We show that only a few variables are able to robustly correlate with these outcomes. To understand the relationship between the potential correlates in explaining the infection and death rates, we create a Jointness Space. Using this space, we conclude that the extent to which each variable is able to provide a credible explanation for the COVID-19 infections/mortality outcome varies between countries because of their heterogeneous features.

Graphlets in Multiplex Networks.

We develop graphlet analysis for multiplex networks and discuss how this analysis can be extended to multilayer and multilevel networks as well as to graphs with node and/or link categorical attributes. The analysis has been adapted for two typical examples of multiplexes: economic trade data represented as a 957-plex network and 75 social networks each represented as a 12-plex network. We show that wedges (open triads) occur more often in economic trade networks than in social networks, indicating the tendency of a country to produce/trade of a product in local structure of triads which are not closed. Moreover, our analysis provides evidence that the countries with small diversity tend to form correlated triangles. Wedges also appear in the social networks, however the dominant graphlets in social networks are triangles (closed triads). If a multiplex structure indicates a strong tie, the graphlet analysis provides another evidence for the concepts of strong/weak ties and structural holes. In contrast to Granovetter's seminal work on the strength of weak ties, in which it has been documented that the wedges with only strong ties are absent, here we show that for the analyzed 75 social networks, the wedges with only strong ties are not only present but also significantly correlated.

Professor Anastas Kocarev, First Macedonian Oncologist with Worldwide Reputation and Pioneer in Cancer Diagnostics and Treatment with Radium.

PhD. Anastas Kocarev (Kotzareff in French) is one of the most prominent Macedonian doctors and experts, prolific contributor to the cancer research in Switzerland and France in the first decades of the 20th century. He was born in Ohrid on May 5th, 1889. He graduated from the Faculty of Medicine in Geneva where he defended a doctorate in medicine in 1915. In 1916 he was elected Assistant Professor (Private Docent) at that Faculty. He was a prominent scientist and professor of experimental medicine at the Faculty of Medicine in Geneva and the Sorbonne University in Paris, with a wide reputation in Europe and the United States. PhD. A. Kocarev is one of the pioneers of oncology and radiology in the world, a forerunner of modern nuclear medicine and positron emission tomography. He was a close associate of Nobel laureate in chemistry and physics Maria Sklodovska-Curie and at her invitation moved to Paris in 1925 to continue the research on the diagnosis and treatment of cancer using radium. He was fully devoted to science and published numerous scientific papers and books with high citations and dissemination in many medical libraries in Europe and beyond. In addition to his professional teaching and scientific work as a top oncologist-radiologist, he was a great patriot with advanced political ideas. He founded the Academic Society "Macedonia" in Geneva, in 1915, and united it with other Macedonian political associations from Zurich and Lausanne, in 1918, into a joint "Alliance of Macedonian Societies for Independent Macedonia", with commitments, activities and initiatives to the Society of Nations, based in Geneva, Switzerland, for the proper resolution of the Macedonian national issue by creating a united and independent state "Macedonia" or the formation of a "Balkan Federation". He died suddenly in Paris on March 29, 1931.

Graphlet characteristics in directed networks.

Graphlet analysis is part of network theory that does not depend on the choice of the network null model and can provide comprehensive description of the local network structure. Here, we propose a novel method for graphlet-based analysis of directed networks by computing first the signature vector for every vertex in the network and then the graphlet correlation matrix of the network. This analysis has been applied to brain effective connectivity networks by considering both direction and sign (inhibitory or excitatory) of the underlying directed (effective) connectivity. In particular, the signature vectors for brain regions and the graphlet correlation matrices of the brain effective network are computed for 40 healthy subjects and common dependencies are revealed. We found that the signature vectors (node, wedge, and triangle degrees) are dominant for the excitatory effective brain networks. Moreover, by considering only those correlations (or anti correlations) in the correlation matrix that are significant (>0.7 or <-0.7) and are presented in more than 60% of the subjects, we found that excitatory effective brain networks show stronger causal (measured with Granger causality) patterns (G-causes and G-effects) than inhibitory effective brain networks.