Analysis of COVID-19 outbreak based on Window-Time-LSTM Model

CHENG Ning; DING Chang-song; GAO Wan-qing; LIU Jia-jun

doi:10.16462/j.cnki.zhjbkz.2021.05.015

Volume 25 Issue 5

Jun. 2021

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CHENG Ning, DING Chang-song, GAO Wan-qing, LIU Jia-jun. Analysis of COVID-19 outbreak based on Window-Time-LSTM Model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(5): 577-582. doi: 10.16462/j.cnki.zhjbkz.2021.05.015

Citation:

CHENG Ning, DING Chang-song, GAO Wan-qing, LIU Jia-jun. Analysis of COVID-19 outbreak based on Window-Time-LSTM Model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(5): 577-582. doi: 10.16462/j.cnki.zhjbkz.2021.05.015

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Analysis of COVID-19 outbreak based on Window-Time-LSTM Model

doi: 10.16462/j.cnki.zhjbkz.2021.05.015

1.
School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, China
2.
Big Data Analysis Laboratory of Traditional Chinese Medicine in Hunan Province, Changsha 410208, China

Funds:

National Key Research and Development Plan 2017YFC1703306

Key R & D Plan of Hunan Province 2017SK2111

Hunan Provincial Natural Resources Foundation 2018JJ2301

Key Project of Hunan Education Department 18A227

Key Subject of Hunan TCM Research Program 2020002

More Information

Corresponding author: DING Chang-song, E-mail: dingcs1975@hnucm.edu.cn
Received Date: 2020-08-18
Rev Recd Date: 2020-12-16

Available Online: 2021-06-16

Publish Date: 2021-05-10

Abstract

Abstract

Objective To solve the data difference between COVID-19 confirmed cases and actual number of COVID-19 infections, a new model is proposed to predict the spread of the disease. The data difference has been mainly caused by insufficient understanding in the early stage of transmission, limited detection capabilities and the long incubation period. Methods The historical data of the number of confirmed cases are analyzed based on Window-Time. A Long Short-Term Memory (LSTM) network model is combined with the Window-Time strategy to analyze and predict the actual number of infections according to data published of various regions in the world. Results The LSTM network model with Window-Time strategy has higher accuracy than other models. Tuning the width of the Window-Time to the width of 5, the prediction result shows that it is closest to the real actual number of infections, which is consistent with the incubation period of COVID-19 generally known as 3-7 days. Conclusion This method provides a reference for the analysis of the transmission rate of COVID-19 and the incubation period of the epidemic.
- COVID-19,
- Window-Time,
- LSTM network

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References(15)

References

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