Demographic characteristics of the Russian population

The article deals with the results of numerical estimation of demographic characteristics of the Russian female and male populations using the example of the RF 2019 statistical materials with the help of the digital twin for population developed by the author with his colleagues. It demonstrates the capabilities of the digital twin to estimate the attainment of life expectancy limits and predict the demographic characteristics of any population based on previous statistical data of that population. There was used a hardware-software package with embedded the digital twin for the Russian population on the basis of thirty-three years of statistical research into the effects of the total lifestyle index on longevity, workability, ageing and other population characteristics. The life expectancy of the Russian Federation population with the demographic characteristics for 2019 tends to 79 years for men and 122 years for women, with a maximum possible total lifestyle index. For the current demographic characteristics of the population, the rate of population aging from time for different total lifestyle indices has at least one maximum. The possibility of numerical calculation of life expectancy as a function of changes in the average weight of the male and female population is shown. The possibility of controlling the biological age of a population member depending on his total lifestyle index is shown. Developed hardware-software complex based on digital twin for population estimation of demographic characteristics of populations can be used to calculate and predict the demographic characteristics of any population on the basis of previous statistical data of this population. Hardware-software complex can be useful for public and state organizations, statistical agencies, medical organizations of different profiles. insurance companies, staff recruitment companies, pension funds, venture capital funds, private finance funds, private banks and capital management organizations.

population, digital twin, biological age, life expectancy, workability, aging rate, lifestyle, health resource, quality of life