Predicting The Transmission Of Novel Coronavirus Pneumonia Based On Deep Learning Model

The COVID-19 epidemic since 2019 has had a major impact on areas such as health and medical infrastructure,and the economy.The infection of the new coronavirus pneumonia is mainly respiratory transmission.The transmission power of the new coronavirus is higher than the transmission speed and ability of the previous SASR,and it is more infectious than the SASR.Because its nature is respiratory transmission,under the action of other external factors,the transmission speed is fast and the transmission range is relatively wide,and the health of the people is also seriously threatened.Without intervening quarantines,the virus could quickly spread to thousands of people.Through early detection,early isolation,and early treatment,the possibility of harm caused by virus transmission is minimized.Due to the complexity of infection spread and the traces of infected people,there is no reliable calculation and mathematical model for the spread of new coronavirus pneumonia.In addition,the lack of more reliable data collection and reporting makes modeling attempts difficult.Therefore,it is of great research significance to use reliable data sources and innovative prediction models to predict the transmission route of novel coronavirus pneumonia.This paper proposes a model based on two different conditions for predicting the spread of 2019-n Co V.This paper finds that deep learning models are very suitable for spatiotemporal series modeling,and proposes two methods to predict the new coronavirus pneumonia transmission model.In the third chapter,this paper applies the cellular automata and SEIR model.This paper combines the characteristics of the new coronavirus with close contact transmission and long incubation period,and combines the cellular automata and SEIR model to study the population.The influence of the total amount and infectious population characteristics on the prediction of the infectivity of susceptible individuals,and analyze the influence of the intervention measures,intervention time,intervention intensity,and changes in the diagnosis criteria on the transmission trend of the new coronavirus.Conditions that consider the impact of isolation measures,asymptomatic infections,and cell space size on virus transmission.The results of the third chapter show that the simulated data and the actual data are in good agreement,providing better performance accuracy.In Chapter 4,this paper proposes to predict the novel coronavirus based on Long Short-Term Memory(LSTM)model,Bidirectional LSTM(BD-LSTM)model and Encoder-Decoder LSTM(ED-LSTM)model Propagation,this model is suitable for conditions that do not take into account the effect of long-distance travel on virus transmission,and the interference of population density on the model.This article selects 4countries,China,Italy,the United States and India,for the prediction of the spread of new coronavirus pneumonia.Chapter 4 In univariate models,ED-LSTM provides better performance accuracy in most cases.While in multivariate models,BD-LSTM and ED-LSTM can provide better performance accuracy in most cases.