Analysis And Comparison Of Two Kinds Of COVID-19 Prediction Models

The widespread of the coronavirus disease 2019(COVID-19)affected the social stability and economic development of many countries.Analyzing and predicting the development of the epidemic with suitable infectious disease model can help people better respond to the epidemic.This thesis mainly surveys two mechanistic models applied to COVOD-19 that we will refer to here as the SEIR model and the HIT model.The SEIR model divides the population into four categories:susceptible,exposed,infectious,and removed.It conforms to the feature that the COVID-19 has a long latent period,so the SEIR model is selected to predict the epidemic.However,the traditional SEIR model cannot reflect the important feature that the COVID-19 is also highly contagious during the latent period,so this thesis has made adjustments to the classic SEIR model by adding a parameter?₂to describe the infectivity of patients in the latent period,which resulted in a modified SEIR model.Compared with the traditional SEIR model,the modified SEIR model can more accurately describe the spread of the COVID-19 in the population.Secondly,this thesis surveys the HIT model which is a infectious disease prediction model developed by Yurii Nesterov in response to the epidemic.It is a discrete-time linear switching model.By adjusting the parameters,the HIT model can accurately predict the number of confirmed cases in the near future.The prediction results of the two models are compared with the same epidemic data in Belgium.The advantages and disadvantages of the two models are summarized.We also apply the output values in the HIT model to the modified SEIR model to improve the accuracy of the modified SEIR model's prediction in the epidemic.