Considering the widespread of Covid-19 and its impact on the population health in Russian regions, it is necessary to examine the impact of the pandemic (as excess mortality) on the regional socio-economic development in 2020. Based on a quantitative and qualitative model, the study explains the process of coronavirus diffusion at the regional level, using information from foreign publications, Russian regional statistics and a database of legal documents «Consultant +». The concept of spatial diffusion, developed in the 1950s-1980s, was chosen as the research methodology. The study methods include a cartographic analysis of the monthly dynamics of coronavirus spread in Russian regions and regression analysis of regional differences in excess mortality regarding the most significant explanatory variables. The developed regression model explains the spread of Covid-19 across Russian regions in 2020, while the proposed qualitative model «network-place-scaling» describes the spatial diffusion of the virus. The conducted analysis confirmed the relationship between the spread of the virus and economic specialisation of regions. Simultaneously, such widely discussed factors as physical density, urbanisation level and per capita income did not show significant correlation with excess mortality. The study revealed the following results. There is a significant discrepancy between the actual situation in Russian regions and expected developments according to the simplified centre-periphery model. The important regression variables, explaining the interregional differences in excess mortality in 2020, include the share of employed in contact-intensive wholesale and retail trade and manufacturing (large production teams); proportion of the population over 65; the number of retail facilities per 1000 people. The qualitative model «network-place-scaling» was deemed suitable for explaining the mechanisms of the spread of coronavirus in Russian regions. Future studies should focus on examining the mechanisms and socio-economic consequences of the pandemic at the municipal level of large cities and urban agglomerations in Russia.
Intensive socio-economic interactions are a prerequisite for the innovative development of the economy, but at the same time, they may lead to increased epidemiological risks. Persistent migration patterns, the socio-demographic composition of the population, income level, and employment structure by type of economic activity determine the intensity of socio-economic interactions and, therefore, the spread of COVID-19.We used the excess mortality (mortality from April 2020 to February 2021 compared to the five-year mean) as an indicator of deaths caused directly and indirectly by COVID-19. Similar to some other countries, due to irregularities and discrepancies in the reported infection rates, excess mortality is currently the only available and reliable indicator of the impact of the COVID-19 pandemic in Russia.We used the regional level data and fit regression models to identify the socio-economic factors that determined the impact of the pandemic. We used ordinary least squares as a baseline model and a selection of spatial models to account for spatial autocorrelation of dependent and independent variables as well as the error terms.Based on the comparison of AICc (corrected Akaike information criterion) and standard error values, it was found that SEM (spatial error model) is the best option with reliably significant coefficients. Our results show that the most critical factors that increase the excess mortality are the share of the elderly population and the employment structure represented by the share of employees in manufacturing (C economic activity according to European Skills, Competences, and Occupations (ESCO) v1 classification). High humidity as a proxy for temperature and a high number of retail locations per capita reduce the excess mortality. Except for the share of the elderly, most identified factors influence the opportunities and necessities of human interaction and the associated excess mortality.
The COVID-19 pandemic has demonstrated that the lack of consideration of the local specifics of territories, such as the specifics of socio-economic interactions, labor market characteristics, leads to serious social or economic consequences when developing response measures to epidemiological threats. The creation of a typology of territories (urban districts / okrugs) makes it possible to more accurately select measures to regulate socio-economic interactions in the event of future complications of the epidemiological situation. Clustering of municipalities according to a set of local factors that significantly explain the severity of the pandemic in the first year made it possible to identify three types of urban districts that differ in population size and intensity of socio-economic interactions (SEI): these are key service centers with a high intensity of SEI, local centers with medium SEI intensity, small towns with low SEI intensity
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