Dynamic Analysis And Modeling Of COVID-19 Based On Complex Network

New coronary pneumonia(COVID-19)has been spreading globally as an emerging infectious disease since the end of 2019.As of March 2022,the cumulative number of diagnosed cases worldwide has reached 400 million.The spread and prevalence of COVID-19 poses a significant threat to human society regarding life safety and property damage and may become a long-standing infectious disease.The modeling prediction and quantitative analysis can help reveal the epidemic pattern of COVID-19,which is an essential reference for government departments to make decisions on epidemic prevention and control.This paper constructs a modified model for the COVID-19 outbreak based on the SEIR model.It investigates the transmission trends and influencing factors of the COVID-19 epidemic from the differential equation model and the network model,respectively.The main research contents are summarized as follows:(1)In response to the emergence of asymptomatic infected patients in the COVID19 outbreak,this paper proposes a modified SEIR model by adding self-isolated populations,asymptomatic infected patients and hospitalized isolated populations.Based on the actual data of the epidemic announced in Hubei Province,the model parameters were fitted by the least-squares method,and the model was solved by the fourth-order Longo-Kuta formula.By comparing with the actual data,it was confirmed that the expanded model has high fitting accuracy and can be used to characterize the development trend of the COVID-19 epidemic.Analysis of key model parameters using the modified SEIR model revealed that increasing the isolation rate of susceptible populations,increasing the attendance rate of infected patients,and reducing the proportion of asymptomatic infected patients played an important role in containing the outbreak.The effectiveness of the outbreak prevention and control policies in Zhejiang,Anhui,Henan,and Hubei provinces was also evaluated,respectively.(2)To address the scale-free characteristics of virtual social networks,this paper develops a COVID-19 dynamics model on BA scale-free networks.With the progressive understanding of COVID-19,asymptomatic infections were further classified into asymptomatic infections that were always undetected,and asymptomatic infections that tested positive for nucleic acids.A kinetic model on the BA scale-free network was proposed.COVID-19 propagation simulations are performed on BA scale-free networks according to the model parameters,network generation rules and propagation mechanisms.Simulation results show that controlling the population exposure rate,improving the timeliness of prevention and control policy implementation,adjusting network generation parameters,and isolating hub nodes all affect the spread of COVID-19 on the network,and give theoretical guidance for developing prevention and control policies based on their effects.(3)To address the small-world characteristics of virtual social networks,this paper develops a COVID-19 dynamics model on WS small-world networks.The model and parameter settings are kept the same as those on the BA scale-free network The COVID-19 propagation simulation is performed on the WS small-world network according to the model parameters,network generation rules,and propagation mechanism.Simulation results show that controlling the population exposure rate,the initial number of infections,adjusting the network generation parameters,and introducing a feedback mechanism all affect the spread of COVID-19 on the WS smallworld network.Moreover,the essential difference between the two types of complex networks lies in the difference in degree distribution.The characteristics of COVID-19 propagation on different networks are compared and analyzed to provide theoretical guidance for formulating more comprehensive prevention and control policies.