Analysis and Prediction of COVID-19 based on the SEIRV Model

In the past two years, with the continuous variation and development of COVID-19 in the world, and its vigorous study by various countries, some new features in its spread and popularity have emerged. So based on the traditional SEIR model, an improved SEIRV(Susceptible, Exposed, Infected, Recovered and Vaccinated) model is proposed in this paper, which by adding several corresponding parameters, fully considers the factors of the asymptomatic infected persons and the re-positive patients, as well as the situation of vaccine emergence and large-scale vaccination. The simulation experiments show that screening the asymptomatic infected persons and continuous tracking and detection the re-positive patients play an important role in curbing the epidemic development, and meanwhile raising the vaccination rate can prevent the epidemic development to the greatest extent. Finally, using the actual epidemic data of Italy and the United States combined with SEIRV model for experimental prediction, it can been concluded that the increase of vaccination rate can effectively inhibit the epidemic development and end the spread of COVID-19 as early as possible.