[1]GAN Yu,WU Yu,WANG Jianyong.Epidemics trend prediction model of COVID-19[J].CAAI Transactions on Intelligent Systems,2021,16(3):528-536.[doi:10.11992/tis.202008037]
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Epidemics trend prediction model of COVID-19

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 528-536 Column: 学术论文—人工智能基础 Public date: 2021-05-05
Title:
Epidemics trend prediction model of COVID-19
Author(s):
GAN Yu;  WU Yu;  WANG Jianyong
College of Computer Science, Sichuan University, Chengdu 610065, China
Keywords:
COVID-19; SEIR model; LSTM; intelligent systems; prediction model; real time prediction; neural networks; deep learning
CLC:
TP391.4
DOI:
10.11992/tis.202008037
Abstract:
The outbreak of coronavirus disease 2019 (COVID-19) has threatened and brought a serious impact on the health and daily life of people. If people are warned beforehand about the speed of the disease, they are able to take necessary preventive measures. As one of the most classical epidemic models, the SEIR model can hardly model the spread of COVID-19 and predict its trend because the rate of transmission is constant, which is one of the required parameters of the SEIR model. Aiming at this problem, a dynamic prediction method of the rate of transmission is derived based on long short-term memory (LSTM). An LSTM-SEIR network (LS-Net) is then proposed based on the LSTM and SEIR models to predict the trend of the COVID-19 epidemic. To validate the LS-Net, official epidemiological data released from different domestic areas are collected. The experimental results show that LS-Net can predict the spread of COVID-19 validly and with better performance compared with that of the traditional SEIR model.
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Last Update: 2021-06-25

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