Stochastic Modeling of Dynamics of the Spread of COVID-19 Infection Taking Into Account the Heterogeneity of Population According To Immunological, Clinical and Epidemiological Criteria

Abstract

Here we present a stochastic model of the spread of Covid-19 infection in a certain region. The model is a continuous-discrete random process that takes into account a number of parallel processes, such as the non-stationary influx of latently infected individuals into the region, the passage by individuals of various stages of an infectious disease, the vaccination of the population, and the re-infection of some of the recovered and vaccinated individuals. The duration of stay of individuals in various stages of an infectious disease is described using distributions other than exponential. An algorithm for numerical statistical modeling of the dynamics of the spread of infection among the population of the region based on the Monte Carlo method has been developed. To calibrate the model, we used data describing the level of seroprevalence of the population of the Novosibirsk Region in the first wave of the Covid-19 epidemic in 2020. The results of computational experiments with the model are presented for studying the dynamics of the spread of infection under vaccination of the population of the region.