Prediction of COVID-19 Epidemic in Xi'an based on SEAIQR Model and Dropout-LSTM Model
Objective This study aims to predict the coronavirus disease 2019(COVID-19)epidemic in Xi'an based on SEAIQR model and Dropout-LSTM model,and to provide a scientific basis for evaluating the effectiveness of the"dynamic zero-COVID policy".Methods Considering a large number of asymptomatic infections,the changing parameters,and control procedures,we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-removed(SEAIQR)model with stage-specific interventions.Considering the time-series characteristics of COVID-19 data and the nonlinear relationship between them,we constructed a deep learning Dropout-LSTM model.The data of newly confirmed cases in Xi'an from December 9th,2021 to January 31st,2022 were used to fit the model,and the data from February 1st,2022 to February 7th,2022 were used to evaluate the model performance of forecasting.We then calculated the effective reproduction number(Rt)and analyzed the sensitivity of the different measurement scenarios.Results The peak of newly confirmed cases predicted by the SEAIQR model would appear on December 26th,2021,with 176 cases,and the"dynamic zero-COVID policy"may be achieved in January 24th,2022,with R2=0.849.The Dropout-LSTM model can reflect the time-series and nonlinear characteristics of the data,and the predicted newly confirmed cases were highly consistent with the actual situation,with R2=0.937.The MAE and RMSE of the Dropout-LSTM model were lower than those of the SEAIQR model,indicating that the predicted results were more ideal.At the beginning of the outbreak,R0 was 5.63.Since the implementation of comprehensive control,Rt has shown a gradual downward trend,dropping to below 1.0 on December 27th,2021.With the reduction of effective contact rate,the early implementation of control measures and the improvement of immunity threshold,the peak of newly confirmed cases will continue to decrease.Conclusion The proposed Dropout-LSTM model forecasts the epidemic well,which can provide a reference for decision-making of the"dynamic zero-COVID policy."
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