Skip to main content
SpringerLink
Log in

Forecasting Covid-19 Infections in Ho Chi Minh City Using Recurrent Neural Networks

  • Conference paper
  • First Online:
Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1500))

Included in the following conference series:

  • 1168 Accesses

Abstract

Coronavirus disease (Covid-19) has caused negative impacts on the economy, society and lives of people in Vietnam, especially in Ho Chi Minh City. Forecasting daily new Covid-19 infections is essential and important for prevention and social distancing purposes. Recurrent neural networks have been intensively used to process sequential data like voice, text, video and time series recently. In this paper, we present the forecasting models to predict new Covid-19 infected cases in Ho Chi Minh City using different recurrent neural networks (RNN). The experimental results show that the bidirectional long short-term memory network obtains better performance than the other models based on three statistical assessment criteria, the mean absolute error (MAE), symmetric mean absolute percentage error (sMAPE) and root mean square error (RMSE). The forecasting performance is also verified on the different forecasting horizons and multiple test runs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Petropoulos, F., Makridakis, S., Stylianou, N.: COVID-19: forecasting confirmed cases and deaths with a simple time-series model. Int. J. Forecast (2020)

    Google Scholar 

  2. Maleki, M., Mahmoudi, M.R., Wraith, D., Pho, K.H.: Time series modelling to forecast the confirmed and recovered cases of COVID-19. Travel Med. Infect. Dis. 37, 101742 (2020)

    Google Scholar 

  3. Hamadneh, N.N., et al.: Artificial neural networks for prediction of covid-19 in Saudi Arabia. Comput. Mater. Continua 66(3), 2787–2796 (2021)

    Article  Google Scholar 

  4. Loli Piccolomini, E., Zama, F.: Monitoring Italian COVID-19 spread by a forced SEIRD model. PloS One 15(8), e0237417 (2020)

    Google Scholar 

  5. Good Judgment: COVID Recovery dashboard, Good Judgment (2020). https://goodjudgment.com/covidrecovery/

  6. Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  7. Hochreiter, S., Schmidhuber, J.: Long shortterm memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  8. Karpathy, A., Johnson, J., Li, F.-F.: Visualizing and understanding recurrent networks. arXiv preprint (2015)

    Google Scholar 

  9. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1724–1734 (2014)

    Google Scholar 

  10. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014)

    Google Scholar 

  11. Gruber, N., Jockisch, A.: Are GRU cells More specific and LSTM cells more sensitive in motive classification of text? Front. Artif. Intell. 3, 40 (2020)

    Article  Google Scholar 

  12. Li, X., Wu, X.: Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, QLD, pp. 4520–4524 (2015)

    Google Scholar 

  13. Dua, M., Yadav, R., Mamgai, D., Brodiya, S.: An improved RNN-LSTM based novel approach for sheet music generation. Procedia Comput. Sci. 171, 465–474 (2020)

    Article  Google Scholar 

  14. Shen, Z., Bao, W., Huang, D.S.: Recurrent neural network for predicting transcription factor binding sites. Sci. Rep. 8, 15270 (2018)

    Article  Google Scholar 

  15. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural. Inf. Process. Syst. 27, 3104–3112 (2014)

    Google Scholar 

  16. Ullah, A., Ahmad, J., Muhammad, K., Sajjad, M., Baik, S.W.: Action recognition in video sequences using deep bi-directional LSTM with CNN features. IEEE Access 6, 1155–1166 (2017)

    Article  Google Scholar 

  17. Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016)

    Article  Google Scholar 

  18. Wang, J., Liu, T., Luo, X., Wang, L.: An LSTM approach to short text sentiment classification with word embeddings. In: Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018), Hsinchu, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), pp. 214–223 (2018)

    Google Scholar 

  19. Nguyen, D.Q., Phan, M.N., Zelinka, I.: Forecasting time series with long short-term memory networks. Can Tho Univ. J. Sci. 12(2), 53–59 (2020)

    Google Scholar 

  20. Kim, T.-Y., Cho, S.-B.: Web traffic anomaly detection using C-LSTM neural networks. Expert Syst. Appl. 106, 66–76 (2018)

    Article  Google Scholar 

  21. Wu, Y., Yuan, M., Dong, S., Lin, L., Liu, Y.: Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing 275, 167–179 (2018)

    Article  Google Scholar 

  22. Makridakis, S., Hibon, M.: The M3-Competition: results, conclusions and implications. Int. J. Forecast. 16(4), 451–476 (2000)

    Article  Google Scholar 

  23. Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The M4 competition: results, findings, conclusion and way forward. Int. J. Forecast. 34(4), 802–808 (2018)

    Article  Google Scholar 

  24. Nguyen D.Q., Phan M.N., Zelinka I.: Periodic time series forecasting with bidirectional long short-term memory. In: The 5th International Conference on Machine Learning and Soft Computing (ICMLSC 2021), ACM International Conference Proceeding Series (ISBN 978-1-4503-8761-3), pp. 60–64 (2021)

    Google Scholar 

  25. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Van Lang University, 45 Nguyen Khac Nhu, Co Giang Ward, District 1, Ho Chi Minh City, Vietnam

    Quoc-Dung Nguyen & Hung-Tien Le

Authors
  1. Quoc-Dung Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hung-Tien Le
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quoc-Dung Nguyen .

Editor information

Editors and Affiliations

  1. HCMC University of Technology (HCMUT), Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

  4. Hosei University, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nguyen, QD., Le, HT. (2021). Forecasting Covid-19 Infections in Ho Chi Minh City Using Recurrent Neural Networks. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-8062-5_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8061-8

  • Online ISBN: 978-981-16-8062-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation