A simulation model for the propagation of Covid-19 virus based on the discrete-time Markov chain

The prevalence of infectious diseases in the community depends on various factors, including the severity of the disease, compliance with health and communication protocols, vaccination rate, impact factor and duration of its effectiveness on immunization, and effectiveness of treatment protocols and average duration of treatment. Having a model based on which the behavior of the disease can be predicted according to various parameters, can help community leaders to deal with these types of diseases. In the studies conducted so far in this field, the impact of some factors such as compliance with health guidelines, the latent period of the disease and immunity after the disease at the same time as the main factors such as the prevalence rate and vaccination rate have not been fully considered. In this paper, a new and comprehensive model based on Markov theory is presented for predicting the behavior of the Covid-19 disease. This model can imitate the behavior of the disease in different conditions by receiving the given parameters. Numerous simulations with different values of input parameters and their similarity to the actual behavior of Covid-19 disease show the accuracy of the model.