![typical corona weather typical corona weather](https://www.sleeping-out.co.za/ftp/Maps/24430-GM.jpg)
Understanding the parameters that influence the course of the pandemic is of paramount importance in the ongoing worldwide attempts to minimize the devastating effects of the virus which, to the present moment, has already taken a toll of more than a million lives ( Dong et al., 2020), and resulted in double-digit recession among some of the major world economies ( World Bank, 2020a). The ancient wisdom teaches us that “knowing your adversary” is essential in every battle-and this equally applies to the current global struggle against the COVID-19 pandemic. Detailed comparisons of obtained results with previous findings, and limitations of our approach, are also provided. 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. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. 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 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. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive number R 0 (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 here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. 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. Yet, this is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. It is hard to overstate the importance of a timely prediction of the COVID-19 pandemic progression. 3Department for Medical Statistics and Informatics, School of Medicine, University of Belgrade, Belgrade, Serbia.2Quantitative Biology Group, Faculty of Biology, University of Belgrade, Belgrade, Serbia.1Institute of Physics Belgrade, National Institute of the Republic of Serbia, University of Belgrade, Belgrade, Serbia.Igor Salom 1, Andjela Rodic 2, Ognjen Milicevic 3, Dusan Zigic 1, Magdalena Djordjevic 1 and Marko Djordjevic 2 *