SEIARN: Intelligent Early Warning Model of Epidemic Spread Based on LSTM Trajectory Prediction

Liya Wang, Yaxun Dai, Renzhuo Wang, Yuwen Sun, Chunying Zhang (), Zhiwei Yang and Yuqing Sun
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Liya Wang: College of Science, North China University of Science and Technology, Tangshan 063210, China
Yaxun Dai: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Renzhuo Wang: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Yuwen Sun: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Chunying Zhang: College of Science, North China University of Science and Technology, Tangshan 063210, China
Zhiwei Yang: College of Science, North China University of Science and Technology, Tangshan 063210, China
Yuqing Sun: College of Economics, North China University of Science and Technology, Tangshan 063210, China

Mathematics, 2022, vol. 10, issue 17, 1-23

Abstract: A SEIARN compartment model with the asymptomatic infection and secondary infection is proposed to predict the trend of COVID-19 more accurately. The model is extended according to the propagation characteristics of the novel coronavirus, the concepts of the asymptomatic infected compartment and secondary infection are introduced, and the contact rate parameters of the improved model are updated in real time by using the LSTM trajectory, in order to make accurate predictions. This SEIARN model first builds on the traditional SEIR compartment model, taking into account the asymptomatic infection compartment and secondary infection. Secondly, it considers the disorder of the trajectory and uses the improved LSTM model to predict the future trajectory of the current patients and cross-track with the susceptible patients to obtain the contact rate. Then, we conduct real-time updating of exposure rates in the SEIARN model and simulation of epidemic trends in Tianjin, Xi’an, and Shijiazhuang. Finally, the comparison experiments show that the SEIARN model performs better in prediction accuracy, MSE, and RMSE.

Keywords: COVID-19; LSTM model; SEIARN model; trajectory intersection; asymptomatic infection; secondary infection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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