Abstract
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.
Article PDF
Avoid common mistakes on your manuscript.
References
J. Ji, U. Bae, Economic outlook using infectious disease spread model, Bank of Korea Monthly Survey Statistics, 2020, pp. 16–38. Available from: https://www.bok.or.kr/portal/cmmn/file/fileDown.domenuNo=200438&atchFileId=FILE_000000000018338&fileSn=1.
V.Y. Fan, D.T. Jamison, L.H. Summers, Pandemic risk: how large are the expected losses?, Bull. World Health Organ. 96 (2018), 129–134.
M.I. Meltzer, N.J. Cox, K. Fukuda, The economic impact of pandemic influenza in the United States: priorities for intervention, Emerg. Infect. Dis. 5 (1999), 659–671.
N.A.M. Molinari, I.R. Ortega-Sanchez, M.L. Messonnier, W.W. Thompson, P.M. Wortley, E. Weintraub, et al. The annual impact of seasonal influenza in the US: measuring disease burden and costs, Vaccine 25 (2007), 5086–5096.
S.K. Peasah, E. Azziz-Baumgartner, J. Breese, M.I. Meltzer, M.A. Widdowson, Influenza cost and cost-effectiveness studies globally – a review, Vaccine 31 (2013), 5339–5348.
F. Prager, D. Wei, A. Rose, Total economic consequences of an influenza outbreak in the United States, Risk Anal. 37 (2017), 4–19.
J.T. Liu, J.K. Hammitt, J.D. Wang, M.W. Tsou, Valuation of the risk of SARS in Taiwan, Health Econ. 14 (2005), 83–91.
A. Delivorias, N. Scholz, Economic impact of epidemics and pandemics, Eur. Parliament. Res. Serv. Research Paper, PE 646.195., 2020, pp. 1–10.
M. Nicola, Z. Alsafi, C. Sohrabi, A. Kerwan, A. Al-Jabir, C. Iosifidis, et al. The socio-economic implications of the coronavirus pandemic (COVID-19): a review, Int. J. Surg. 78 (2020), 185–193.
T.R. Cook, A.S. Hall, Macroeconomic indicator forecasting with deep neural networks, Federal Reserve Bank of Kansas City, Research Working Paper 17-11, 2017.
J.K. Jung, M. Patnam, A. Ter-Martirosyan, An algorithmic crystal ball: forecasts-based on machine learning, International Monetary Fund, 2018. Available from: https://www.imf.org/en/Publications/WP/Issues/2018/11/01/An-Algorithmic-Crystal-Ball-Forecasts-based-on-Machine-Learning-46288.
X. Ding, Y. Zhang, T. Lin, J. Duan, Deep learning for event-driven stock prediction, Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015. Available from: https://www.ijcai.org/Proceedings/15/Papers/329.pdf.
D.N. Pham, S.B. Park, PRICAI 2014: trends in artificial intelligence, Proceedings of the 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI), Vol. 8862, Springer, Gold Coast, QLD, Australia, 2014.
T. Kuremoto, S. Kimura, K. Kobayashi, M. Obayashi, Time series forecasting using a deep belief network with restricted Boltzmann machines, Neurocomputing 137 (2014), 47–56.
W. Bao, J. Yue, Y. Rao, A deep learning framework for financial time series using stacked autoencoders and long-short term memory, PloS One 12 (2017), e0180944.
S.B. Kim, S.E. Kim, An empirical study on economic growth model using deep learning, Korean Credit-card Acad. Soc. 12 (2018), 67–88.
A.M. Schäfer, H.G. Zimmermann, Recurrent neural networks are universal approximators, In: S.D. Kollias, A. Stafylopatis, W. Duch, Oja E. (Eds.), Artificial Neural Networks, International Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, 2006, pp. 632–640.
R. Pascanu, C. Gulcehre, K. Cho, Y. Bengio, How to construct deep recurrent neural networks, arXiv preprint arXiv:1312.6026, 2013. Available from: https://arxiv.org/pdf/1312.6026.pdf.
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (1997), 1735–1780.
H. Hewamalage, C. Bergmeir, K. Bandara, Recurrent neural networks for time series forecasting: current status and future directions, Int. J. Forecast. 37 (2020), 388–427.
M. Schuster, K.K. Paliwal, Bidirectional recurrent neural networks, IEEE Trans. Signal Process. 45 (1997), 2673–2681.
F.A. Gers, J. Schmidhuber, F. Cummins, Learning to forget: continual prediction with LSTM, Proceedings of the 1999 Ninth International Conference on Artificial Neural Networks (ICANN), IET, Edinburgh, UK, 1999, pp. 850–855.
T. Chai, R.R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature, Geosci. Model Dev. 7 (2014), 1247–1250.
J. Benesty, J. Chen, Y. Huang, I. Cohen, Pearson correlation coefficient. In: Noise reduction in speech processing, Springer, Berlin, Heidelberg, 2009, pp. 1–4.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
About this article
Cite this article
Kim, M.H., Kim, J.H., Lee, K. et al. The Prediction of COVID-19 Using LSTM Algorithms. Int J Netw Distrib Comput 9, 19–24 (2021). https://doi.org/10.2991/ijndc.k.201218.003
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.2991/ijndc.k.201218.003