It is hard to overstate the importance of a timely prediction of the COVID-19 pandemic progression. Yet, this is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. Studies addressing parameters that may influence COVID-19 progression relied on either the total numbers of detected cases and similar proxies (which are highly sensitive to the testing capacity, levels of introduced social distancing measures, etc.), and/or a small number of analyzed factors, including analysis of regions that display a narrow range of these parameters. We here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive number R0 (i.e., the measure of COVID-19 inherent biological transmissibility), applying to the completely naïve population in the absence of social distancing, for 118 different countries. We then use bioinformatics methods to systematically collect data on a large number of potentially interesting demographics and weather parameters for these countries (where data was available), and seek their correlations with the rate of COVID-19 spread. While some of the already reported or assumed tendencies (e.g., negative correlation of transmissibility with temperature and humidity, significant correlation with UV, generally positive correlation with pollution levels) are also confirmed by our analysis, we report a number of both novel results and those that help settle existing disputes: the absence of dependence on wind speed and air pressure, negative correlation with precipitation; significant positive correlation with society development level (human development index) irrespective of testing policies, and percent of the urban population, but absence of correlation with population density per se. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. Significant tendencies with health-related factors are reported, including a detailed analysis of the blood type group showing consistent tendencies on Rh factor, and a strong positive correlation of transmissibility with cholesterol levels. Detailed comparisons of obtained results with previous findings, and limitations of our approach, are also provided.

Dynamical energy loss formalism allows generating state-of-the-art suppression predictions in finite size QCD medium, employing a sophisticated model of high-p ⊥ parton interactions with QGP. We here report a major step of introducing medium evolution in the formalism though 1 + 1D Bjorken ("B") expansion, while preserving all complex features of the original dynamical energy loss framework. We use this framework to provide joint R AA and v 2 predictions, for the first time within the dynamical energy loss formalism in evolving QCD medium. The predictions are generated for a wide range of observables, i.e. for all types of probes (both light and heavy) and for all centrality regions in both P b + P b and Xe + Xe collisions at the LHC. Where experimental data are available, DREENA-B framework leads to a good joint agreement with v 2 and R AA data. Such agreement is encouraging, i.e. may lead us closer to resolving v 2 puzzle (difficulty of previous models to jointly explain R AA and v 2 data), though this still remains to be thoroughly tested by including state-of-the-art medium evolution within DREENA framework. While introducing medium evolution significantly changes v 2 predictions, R AA predictions remain robust and moreover in a good agreement with the experimental data; R AA observable is therefore suitable for calibrating parton-medium interaction model, independently from the medium evolution. Finally, for heavy flavor, we observe a strikingly similar signature of the dead-cone effect on both R AA and v 2 -we also provide a simple analytical understanding behind this result. Overall, the results presented here indicate that DREENA framework is a reliable tool for QGP tomography. *

a novel and effective approach in revealing infection progression mechanisms that may be a valuable alternative to detailed numerical simulations.We start by introducing our COVID-19 dynamics model.We then extract COVID-19 count data [7] and select those countries that systematically trace not only confirmed cases and fatalities, but also active cases