Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)

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The LSTM Based Trend Prediction Model: A Case Study of Beijing COVID-19 Epidemic Data

Authors
Ruiling Zhao1, Linchao Yang2, *
1Discipline Inspection and Audit Division, Aviation General Hospital, Beijing, 100012, China
2School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
*Corresponding author. Email: yanglinchao@buaa.edu.cn
Corresponding Author
Linchao Yang
Available Online 29 December 2022.
DOI
10.2991/978-2-494069-31-2_82How to use a DOI?
Keywords
COVID-19; Data Analysis; LSTM; Prediction Model
Abstract

The paper tries to build a COVID-19 trend prediction model with data mining methods and deep learning algorithm by taking Beijing COVID-19 epidemic data as an example. Currently, the Corona Virus Disease 2019 (COVID-19) is spreading around the world, posing a huge threat to the human society. By studying the epidemic curve, the overseas imported cases and incidence rate in different age groups, we draw the conclusion that the overseas imported cases are the main reason for the second rise of the cumulative confirmed cases curve and the incidence rate of the older people over 60 in Beijing is the highest. To support the prevention and control of the COVID-19, we extract the daily cumulative confirmed cases in Beijing from January 20th, 2020 to April 26th, 2020 to establish a trend prediction model based on LSTM method (Long Short-Term Memory). And we compared the proposed LSTM based prediction model with the Support Vector Regression (SVR) based prediction model and the Autoregressive Integrated Moving Average (ARIMA) based prediction model. The result shows the effectiveness of the proposed model.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
29 December 2022
ISBN
978-2-494069-31-2
ISSN
2352-5398
DOI
10.2991/978-2-494069-31-2_82How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ruiling Zhao
AU  - Linchao Yang
PY  - 2022
DA  - 2022/12/29
TI  - The LSTM Based Trend Prediction Model: A Case Study of Beijing COVID-19 Epidemic Data
BT  - Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)
PB  - Atlantis Press
SP  - 672
EP  - 681
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-31-2_82
DO  - 10.2991/978-2-494069-31-2_82
ID  - Zhao2022
ER  -