MODELING OF CORONA VIRUS DISEASE (COVID-19)

ABSTRACT

Coronavirus poses a significant threat to public health and the global economy. Understanding the dynamics of the disease spread is crucial in developing effective prevention, control, and eradication strategies. In this work, deterministic and stochastic Susceptible, Exposed, Asymptomatic Infection, Symptomatic Infection, Quarantine and Recovered (𝑆𝐸𝐼𝐴𝐼𝑆 𝑄𝑅) models were proposed to study the transmission dynamics of the Coronavirus Disease (Covid-19). The deterministic model was formulated by a system of Ordinary Differential Equations (ODEs). While the stochastic model was formulated by a Continuous Time Markov Chain (CTMC) that is derived based on the ODE model with constant parameters. A Continuous Time Markov Chain (CTMC) model was developed to further analyze the transmission dynamics. A multi-type branching process was used to obtain an analytical estimate for the probability of a disease extinction and outbreak.