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- W4387194163 abstract "The break out of Corona Virus Disease 2019 has caused great danger and effect for people's health and life. Therefore, predicting the trend of the epidemic is crucial for politicians and medical conductors go for make correct policy ahead of time. The SEIR model is now a more mature epidemic prediction model, but the dynamic prediction of the outbreak cannot be achieved because in this model, the infection rate of virus can be calculated mathematically. Based on this problem, this thesis proposes a CLS-Net model to predict the trend of global epidemic of Corona Virus Disease 2019 under the influence of human mobility. The model extracts features from a large amount of population migration data by CNN model, and then predicts the transmission rate of New Coronavirus in real time with the excellent time-series learning ability of LSTM. Finally, the live updated virus infection rate apply into the SEIR model to realize the dynamic prediction of epidemic development trend. To validate the approach proposed in paper, the statistical data of neocrown pneumonia from various countries around the world were collected for experiments. Take Mean Absolute Error (MAE), R2(R-Squared) and Root Mean Square Error (RMSE) as the evaluation indexes of the model in this paper. The experimental results show that the model proposed in this paper has better performance compared with the classical infectious disease prediction model." @default.
- W4387194163 created "2023-09-30" @default.
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- W4387194163 date "2023-04-01" @default.
- W4387194163 modified "2023-09-30" @default.
- W4387194163 title "COVID-19 Trend Prediction Using CLS-Net Hybrid Model" @default.
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- W4387194163 doi "https://doi.org/10.1109/ictech58362.2023.00061" @default.
- W4387194163 hasPublicationYear "2023" @default.
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