The Prediction of COVID-19 Using LSTM Algorithms
- https://doi.org/10.2991/ijndc.k.201218.003How to use a DOI?
- COVID-19, prediction, RNN, LSTM, economic effects
As COVID-19 enters the pandemic stage, the resulting infections, deaths and economic shocks are emerging. To minimize anxiety and uncertainty about socio-economic damage caused by the COVID-19 pandemic, it is necessary to reasonably predict the economic impact of future disease trends by scientific means. Based on previous cases of epidemic (such as influenza) and economic trends, this study has established an epidemic disease spread model and economic situation prediction model. Based on this model, the author also predict the economic impact of future COVID-19 spread. The results of this study are as follows. First, the deep learning-based economic impact prediction model, which was built based on historical infectious disease data, was verified with verification data to ensure 77% accuracy in predicting inflation rates. Second, based on the economic impact prediction model of the deep learning-based infectious disease, the author presented the COVID-19 trend and future economic impact prediction results for the next 1 year. Currently, most of the published studies on COVID-19 are on the prediction of disease spread by statistical mathematical calculations. This study is expected to be used as an empirical reference to efficient and preemptive decision making by predicting the spread of diseases and economic conditions related to COVID-19 using deep learning technology and historical infectious disease data.
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Myung Hwa Kim AU - Ju Hyung Kim AU - Kyoungjin Lee AU - Gwang-Yong Gim PY - 2021 DA - 2021/01 TI - The Prediction of COVID-19 Using LSTM Algorithms JO - International Journal of Networked and Distributed Computing SP - 19 EP - 24 VL - 9 IS - 1 SN - 2211-7946 UR - https://doi.org/10.2991/ijndc.k.201218.003 DO - https://doi.org/10.2991/ijndc.k.201218.003 ID - Kim2021 ER -