Study On The Transmission And Evolution Of COVID-19 Epidemic Based On SEIR Model And Social Network

Throughout ancient and modern times,various infectious diseases have been endangering people’s lives and health.At the end of 2019,the COVID-19 broke out in Wuhan,and spread to the whole country in a very short time,eventually evolving into a global pandemic.The outbreak of the COVID-19 epidemic has had a serious impact on people’s work and life,and socio-economic activities have almost stagnated.Considering that the spread of the epidemic can be controlled as soon as possible when faced with similar major infectious disease events again in the future,this study takes COVID-19 as an example to deeply analyze the evolution mechanism and spread trend of COVID-19 in the real world,so as to guide people to take scientific and efficient prevention and control measures to curb the development of the epidemic.The main work of the study is as follows:(1)According to the transmission characteristics of COVID-19,the traditional SEIR infectious disease model was improved,and the quarantine Q and the dead D were introduced to make it more consistent with the transmission of COVID-19.Aiming at the problem that the traditional SEIR infectious disease model is not ideal for predicting the trend of epidemic spread,the study proposes a SEIR-C prediction model,which integrates an improved infectious disease model and a deep learning network.Experiments have shown that the model has a good effect on the prediction of epidemic spread.The research also analyzes the isolation measures and vaccines in epidemic prevention and control,providing some reference for the formulation of epidemic prevention and control policies.(2)In view of the spatial transmission characteristics of COVID-19,and in order to better simulate the contact relationship between individuals in reality,the study will build a COVID-19 transmission network on the complex network model,select the BA scale-free network that is most appropriate to the real social situation,and study the transmission of COVID-19 based on scale-free network.Simulation experiments were conducted on factors such as different network topologies,key control nodes,and epidemic control time.Finally,suggestions for epidemic prevention and control were given based on the experimental results.(3)In view of the impact of population mobility on the spread of the COVID-19 epidemic,based on population migration data,the research studies the spread and evolution of the epidemic among urban agglomerations.Taking each province as a node,and population flow as a connecting edge,and using the population migration index as the weight of the edge,the epidemic situation in urban agglomerations is constructed.The epidemic situation is divided into three stages,and the structural characteristics of the epidemic situation transmission network in urban agglomerations in each stage are analyzed.The multi-dimensional characteristics of the node provinces and cities are extracted through social network analysis methods.The spatial characteristics of the epidemic situation are extracted using CNN networks and the temporal characteristics of the epidemic situation are extracted using LSTM networks,Construct an SNACL network to analyze the epidemic transmission trend of node provinces and cities.