Study On The Prediction Of The Spread Scale Of COVID-19 And The Evaluation Of Epidemic Prevention Measures

The spread of infectious diseases in the population has always been an important threat to human health.Simulation modeling of disease transmission and studying the characteristics of infectious disease transmission dynamics can help to better grasp the transmission pattern of infectious diseases and reduce the number of infections.However,in the actual existence of infectious disease transmission process,the simulation of traditional prevention and control measures cannot effectively express the transmission characteristics of this infectious disease of COVID-19,which increases the difficulty of prevention and control of the epidemic;secondly,the influence of random factors such as dynamic exposure of individuals and willingness to prevent the epidemic,the infection data are prone to abnormal fluctuations,and it is difficult to effectively describe the transmission pattern of COVID-19,which leads to the difficulty of simulation results to achieve the expected results and cannot provide effective help for the actual The simulation results are not as effective as expected and cannot provide effective help for the actual epidemic prevention and control.Based on the above problems,this study explores the interrelationship between the hyper network,nucleic acid testing,neoconiosis and secondary infections based on real data from Yangzhou and Yiwu cities,using the classical SEIR model as the main tool,Combined with the national measure of nucleic acid testing,this paper discusses the relationship between hypernetwork,nucleic acid testing,COVID-19 and secondary infection,and uses the hypernetwork as a contact network to conduct in-depth research on the transmission of COVID-19.Specific studies were conducted as follows:Firstly,the classical SEIR model is analyzed and modeled.Secondly,the model is refined by adding the normative measure of nucleic acid testing to make the model more realistic.Then,the impact of nucleic acid testing on the number of infections is investigated based on the difference of total number of people in different regions,the number of days between nucleic acid tests and the number of daily contacts of infected people.The results show that the SEIR model based on nucleic acid detection can help doctors to diagnose patients quickly so that isolation and treatment measures can be taken in time,thus reducing the risk of virus transmission.2.In order to solve the dynamic stochastic propagability problem of epidemic transmission under normal conditions,a hyper network based SEIR model is proposed.Firstly,the modeling method of contact hyper-edge is proposed according to the hyper-network hyperedge characteristics,and the relationship between household contact hyper-edge and work contact hyper-edge is considered,different contact models are constructed and random walks are added to link the two models.Secondly,the number of infections in the supra-edge model was analyzed according to individual differences in prevention and control awareness,secondary infections and different infection rates.Finally,the model was validated through experiments and real case studies in Yiwu.The results show that the proposed method can effectively extract random contact features to achieve accurate prediction of the epidemic in order to develop effective prevention and control measures.Based on the theory of virus transmission dynamics,this paper uses the SEIR model as a means to study real transmission data,focusing on nucleic acid detection measures and the introduction of secondary infection under the hyper network to carry out in-depth research and analyze the impact of epidemic prevention strategies on the number of infections.The research results of this paper are of positive significance to help us predict the scale of epidemic transmission,improve the efficiency of epidemic prevention and control measures,and promote post-epidemic recovery.