Geography, Factors and Consequences of COVID-19 Diffusion in Russia

The chapter describes the spread of the COVID-19 in the Russian regions and its consequences. Two COVID-19 waves could be distinguished, the beginning of the third was recorded. Each wave began in the largest metropolitan areas, where there is a high density of population and interaction, then spread to the periphery. The epidemic affected Russia more than the world average but less than most East-European countries;Moscow was among the most affected areas worldwide. However, the current (real-time) statistics of confirmed cases and deaths may underestimate their real extent due to a number of discussed distortions. Excess mortality in Russia in 2020 corresponded to Latin American countries. It was higher in the least developed Russian regions with high poverty and insufficient health care infrastructure, in the largest agglomerations with an elderly population and in mining regions with a large number of temporary labor migrants. As a result of the epidemic, a special healthcare infrastructure was created and numerous electronic services were developed. However, it also sharpened the debate about restrictive measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

COVID-19: Spatial Dynamics and Diffusion Factors across Russian Regions

Confirmed cases of coronavirus infection, at first approximation, corresponds to models of diffusion of innovations. We applied models to analyze spatial patterns in Russia. The article describes in detail statistical and other restrictions that reduce the possibility of predicting such phenomena and affect decision-making by the authorities. Keeping current trends according to our estimates, as of May 12, the dynamics of confirmed cases will begin to decline in the second half of May, and the end of the active phase of the epidemic, at least in Moscow, can be expected by the end of July. The dynamics of confirmed cases are a reduced and delayed reflection of real processes. Thus, the introduction of a self-isolation regime in Moscow and many other regions has affected the decrease in the number of new confirmed cases in two weeks. In accordance with the model, carriers infected abroad (innovators) were concentrated at the first stage in regions with large agglomerations, in coastal and border regions with a high intensity of internal and external relations. Unfortunately, the infection could not be contained;the stage of exponential growth across the country began. By mid-April 2020, cases of the disease were recorded in all Russian regions;several cases were in the most remote and least connected regions. Among the econometrically identified factors that determine the spread of the disease, one can note a high population density in cities, proximity to the largest metropolitan areas, an increased share of the most active and often traveling part of the population (innovators, migrants), intensive ties within the community and with other countries and regions. The spread rate is higher in regions with a high population exposure to diseases, which confirms the theses on the importance of the region s health capital. Moreover, the combination of factors and their influence changed in accordance with the stages of diffusion, and at the initial stage, random factors prevailed. In conclusion, some directions for further research are given. © 2021 Russian Academy of Sciences. All rights reserved.

COVID-19: Spatial Dynamics and Diffusion Factors across Russian Regions

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.