Dynamic Model And Prediction Of COVID-19 Epidemic

Despite the continuous development of human society and the continuous progress of science and technology,people have never been able to get rid of the attack of infectious diseases.Since 2020,the global epidemic of COVID-19 has unprecedentedly affected people's work and life,severely endangered people's lives and health,and caused tremendous damage to the society and economy.In order to study the transmission mechanism and characteristics of COVID-19 and predict the trend of the epidemic,this paper chooses the COVID-19 epidemic in the United States as the research object,and analyzes and models its epidemic data.Firstly,this paper deeply analyzes the theory of classical ARIMA model,SIR and SEIR dynamic model,and models the actual data.The results show that ARIMA model only has a better prediction effect on data with consistent trends because it completely depends on its latest data;The classical dynamic model has few and fixed parameters,and the data of recovered people is inaccurate,which leads to poor prediction effect.Therefore,based on the dynamic theory of SEIR model,this paper proposes a time-lag convolution SEIRV model.Then,considering that COVID-19 patients also have the ability of infection during the incubation period,and the probability of infection,recovery and death is different every day during the illness,this paper establishes the gamma probability distribution based on the distribution characteristics of the actual statistical data,and obtains the number of daily new confirmed cases and new deaths by convolution.At the same time,ARIMA model is used to model the vaccination data.Considering the different effectiveness between the first vaccination and the second vaccination,a time-lag convolution SEIRV model is constructed.Finally,this paper uses the model to get the probability distribution of infection,recovery and death,the change of the infection rate,fatality rate and effective reproduction number of the COVID-19 epidemic in the United States over time,and makes a three-day step prediction for each day.The average relative error of the prediction of daily new confirmed cases is 1.5%,and the average relative error of the prediction of daily new deaths is 1.8%,The average relative error of 5-day step prediction is 2.8% and 3.3% respectively.The results show that the prediction effect of the model is good,which proves that vaccination plays a significant role in mitigating the epidemic,and has important theoretical significance for the decision-making of epidemic prevention and control.