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.

diffusion of innovations, coronavirus infection, Russian regions, disease factors, health capital, population density, social connections, labor migration, tourism, medical geography

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