In this paper, we present a microsimulation model (MSM) that allows to simulate demographic processes in agent populations. As its input, the model uses a synthetic population, which consists of independent agents with defined dwellings and workplaces. The model allows not only to monitor the changes in demographics of the population over time but also to consider the geospatial distribution of individuals and their activities in a regarded urban setting. Using open-access 2010-2018 demographic data for St. Petersburg, we have assessed the changes in the states of the agents associated with aging, birth, emigration, immigration, and formation of new households. The modelling algorithm is implemented in Python programming language. To demonstrate the capabilities of the model, we derived a synthetic population of Saint-Petersburg for 2018 from the synthetic population of 2010. The results of the modelling are aligned with the available aggregated statistical data. The synthetic populations created with the help of the model allow fine-scale simulations of the outbreaks of influenza and COVID-19 in Russian cities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.