Stochasticization of SEIR model and its implementation in Julia

Modeling the spread of infectious diseases is often done using deterministic compartmental models. However, such models do not account for random uctuations, which are especially important when considering small populations. In this paper, we apply a one-step stochasticization method to the SEIR model. Transitions between susceptible (S), exposed (E), infected (I) and recovered (R) states are represented as interaction schemes. The analytical modeling approach was implemented in the Julia programming language using the Di erentialEquations.jl and Plots.jl libraries. Comparison of stochastic and deterministic models shows the in uence of random e ects on the dynamics of the epidemic process. The proposed stochasticization method can be applied to other mathematical models.