The observed spread of coronavirus infection across Russian regions, as a first approximation, obeys the classic laws of diffusion of innovations. The article describes in detail theoretical approaches to the analysis of the spread of social diseases and discusses methodological limitations that reduce the possibility of predicting such phenomena and affect decision-making by the authorities. At the same time, we believe that for most regions, including Moscow, until May 12, 2020, the dynamics of confirmed cases are a reduced and delayed reflection of actual processes. Thus, the introduced self-isolation regime in Moscow and other agglomerations affected the decrease in the number of newly confirmed cases two weeks after its introduction. In accordance with our model, at the first stage, carriers infected abroad were concentrated in regions with large agglomerations and in coastal and border areas with a high intensity of internal and external links. Unfortunately, the infection could not be contained, and it started growing exponentially across the country. By mid-April 2020, cases of the disease were observed in all Russian regions; however, the remotest regions least connected with other parts of Russia and other countries had only isolated cases. By mid-May, at least in Moscow, the number of new cases began to decline, which created the prerequisites for reducing restrictions on the movement of residents. However, the decrease in the number of new cases after passing the peak of the epidemic in May is slower than the increase at the beginning. These facts contradict the diffusion model; thus, the model is not applicable for epidemiological forecasts based on empirical data. Using econometric methods, it is shown that for different periods of diffusion, various characteristics of the regions affect the spread of the disease. Among these features we note the high population density in cities, proximity to the largest metropolitan areas, higher proportion of the most active and frequently traveling part of the population (innovators, migrants), and intensive ties within the community, as well as with other regions and countries. The virus has spread faster in regions where the population has a higher susceptibility to diseases, which confirms the importance of the region's health capital. The initial stage was dominated by random factors. We conclude this paper with directions for further research.
The article explains the uneven development of small and medium enterprises in Russia within the framework of the ‘entrepreneurial ecosystems’ concept. A corresponding typology of the Russian regions was carried out according to the proposed model. The most developed ecosystems with highdensity and sectoral diversity of SMEs are formed in regions with access to large consumer markets, capital, and low risks for investors. The least developed SMEs sector is in areas with high costs of doing business: the North Caucasus, the Far East and the Arctic zone, which requires special measures of state policy. The level of development of ecosystems determines their ability to withstand external shocks. The effect of the FIFA World Cup is positive in the hosting regions and in its neighbors. Based on the typology, we proposed differentiated support measures.
The COVID-19 pandemic has covered all Russian regions. As of May 8, 2020, about 190 thousand cases have been identified, more than 1600 people with the corresponding diagnosis have died. The values of the indicators are expected to rise. However, the statistics of confirmed cases and deaths may underestimate their actual extent due to testing peculiarities, lagging reporting and other factors. The article identifies and describes the characteristics of the regions in which the incidence and mortality of COVID-19 is higher. Migration of potential carriers of the virus: summer workers and migrant workers from Moscow and large agglomerations, as well as return of labour migrants to the North increase the risks of the disease spread. The risk of mortality is higher in regions with high proportions of the poor and aged residents, for whom it is difficult to adapt to the pandemic, and lower in regions with greater health infrastructure. Based on the revealed patterns, a typology of regions on possible risks is proposed. Above all the risks in and near the largest agglomerations (the cities of Moscow and Saint Petersburg, Moscow and Leningrad Oblasts), in the northern regions where the share of labour migrants is high (Khanty-Mansi and Yamalo-Nenets Autonomous Okrugs), in southern underdeveloped regions (Ingushetia, Karachay-Cherkess, Kabardino-Balkarian Republics, Dagestan, North Ossetia). For the latter, the consequences may be most significant due to the limited capacity to adapt to the pandemic and self-isolation regime, and additional support measures may be required in these regions.
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