720 532
Full Length Article
Journal of Intelligent Systems and Internet of Things
Volume 8 , Issue 2, PP: 20-33 , 2023 | Cite this article as | XML | Html |PDF

Title

Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms

  Khder Alakkari 1 ,   Alhumaima Ali Subhi 2 ,   Hussein Alkattan 3 ,   Ammar Kadi 4 ,   Artem Malinin 5 ,   Irina Potoroko 6 ,   Mostafa Abotaleb 7 ,   El-Sayed M El-kenawy 8 *

1  Department of Statistics and Programming, Faculty of Economics, University of Tishreen, Latakia, P.O. Box 2230, Syria
    (khderalakkari1990@gmail.com)

2  Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk
    (alhumaimaali@uodiyala.edu.iq)

3  Electronic and Computer Center, University of Diyala, Baqubah MJJ2+R9G, Iraq
    (alkattan.hussein92@gmail.com)

4  Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia
    (ammarka89@gmail.com)

5  Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia
    (artemmalinin3@gmail.com)

6  Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia
    (irina_potoroko@mail.ru)

7  Electronic and Computer Center, University of Diyala, Baqubah MJJ2+R9G, Iraq
    (abotalebmostafa@bk.ru)

8  Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
    (skenawy@ieee.org)


Doi   :   https://doi.org/10.54216/JISIoT.080202

Received: August 15, 2021 Accepted February 10, 2023

Abstract :

The COVID-19 epidemic has in fact placed the whole community in a dire predicament that has led to numerous tragedies, including an economic downturn, political unrest, and job losses. Forecasting and identifying COVID-19 infection cases is crucial for the government at all levels because the pandemic grows exponentially and results in fatalities. Hence, by giving information about the spread of the epidemic, the government can move quickly at multiple levels to establish new policies and modalities in order to minimize the trajectory of the COVID-19 pandemic's effects on both public health and the economic sectors. Forecasting models for COVID-19 infection cases in the Ural region in Russia were developed using two deep Long Short-Term Memory (LSTM) learning-based approaches namely Encoder–Decoder LSTM and Attention LSTM algorithms. The models were evaluated based on five standard performance evaluation metrics which include Mean Square Error (MSE), Mean Absolute Error (MAE), Root MSE (RMSE), Relative RMSE (RRMSE), and coefficient of determination (R2). However, the Encoder–Decoder LSTM deep learning-based forecasting model achieved the best performance results (MSE=32794.09, MAE=168.56, RMSE=181.09, RRMSE=13.46, and R2=0.87) compared to the model developed with Attention LSTM models.

Keywords :

COVID-19; LSTM; RMSE;

References :

[1]   Cleary, S. J., Pitchford, S. C., Amison, R. T., Carrington, R., Robaina Cabrera, C. L., Magnen, M., ... & Page, C. P., Animal models of mechanisms of SARS‐CoV‐2 infection and COVID‐19 pathology. British journal of pharmacology, 177(21), 4851-4865, 2020.

[2]   Sachs J., Schmidt-Traub G., Kroll C., Lafortune G., Fuller G., Woelm F., Sustainable Development Report 2020: The Sustainable Development Goals and COVID-19 Includes the SDG Index and Dashboards, Cambridge University Press: Cambridge, UK, 2021.

[3]   Shekerdemian L.S., Mahmood N.R., Wolfe K.K., Riggs B.J., Ross C.E., McKiernan C.A.,  Heidemann S.M., Kleinman L.C.,  Sen A.I.,  Hall M.W.,  et al.,  Characteristics and Outcomes of Children with Coronavirus Disease (COVID-19) Infection Admitted to US and Canadian Pediatric Intensive Care Units. JAMA Pediatr. , 174, 868–873, 2020.

[4]   Upadhyay S. K., Singh R., Singh M., Kumar V., Yadav M., Aggarwal D., Sehrawat N., COVID-19 in republic of India: A report on situation and precautionary strategies to global pandemic. Bull Environ Pharmacol Life Sci, 9(6), 39-48, 2020.

[5]   Koliaki C., Tentolouris A., Eleftheriadou I., Melidonis A., Dimitriadis G.,  Tentolouris  N.,  Clinical management of diabetes mellitus in the era of COVID-19: practical issues, peculiarities and concerns. Journal of clinical medicine, 9(7), 2288, 2020. 

[6]   House, C., Naseefa N., Palissery S., Sebastian H, Corona viruses: A review on SARS, MERS and COVID-19. Microbiol. Insights, 14, 2021.

[7]   Kovoor J. G., Scott N. A., Tivey D. R., Babidge W. J., Scott D. A., Beavis V. S.,  Frydenberg M. , Proposed delay for safe surgery after COVID‐19. ANZ Journal of Surgery, 91(4), 495-506, 2021.

[8]   de Palma, A., & Vosough, S. (2021). Long, medium, and short-term effects of COVID-19 on mobility and lifestyle. CY Cergy Paris Université, cnrs.

[9]   Elhadi M., Alsoufi A., Abusalama A., Alkaseek A., Abdeewi S., Yahya M., ... & Msherghi, A. Epidemiology, outcomes, and utilization of intensive care unit resources for critically ill COVID-19 patients in Libya: A prospective multi-center cohort study. Plos one, 16(4), 2021. 

[10] Pierce J., & Stevens M. P., COVID-19 and antimicrobial stewardship: lessons learned, best practices, and future implications. International Journal of Infectious Diseases, 113, 103-108, 2021.

[11] Fehaid Alqahtani, Mostafa Abotaleb, Ammar Kadi, Tatiana Makarovskikh, Irina Potoroko,   Khder Alakkari, Amr Badr. Hybrid Deep Learning Algorithm for Forecasting SARS‐CoV‐2 Daily Infections and Death Cases. Axioms, 11(620), 1 – 19, 2022.  

[12] Sarker I. H., Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160, 2021.

[13] Mbunge E., Akinnuwesi B., Fashoto S. G., Metfula A. S., Mashwama P., A critical review of emerging technologies for tackling COVID‐19 pandemic. Human behavior and emerging technologies, 3(1), 25-39, 2021. 

[14] Kareem, F. Q., & Abdulazeez, A. M.. Ultrasound medical images classification based on deep learning algorithms: a review. Fusion: Practice and Applications, 3(1), 29-42, 2021 https://doi.org/10.54216/FPA.030102.

[15] Abdulrahma, S. A., & Salem, A. B. M. An efficient deep belief network for Detection of Coronavirus Disease COVID-19. Fusion: Practice and Applications, 2(1), 5-13, 2020 https://doi.org/10.54216/FPA.020102

[16] Pal S., Ghosh S., Nag A. Sentiment analysis in the light of LSTM recurrent neural networks. International Journal of Synthetic Emotions (IJSE), 9(1), 33-39, 2018.

[17] Tanıma Ö., Al-Dulaimi A., Harman A.G.G. Estimating and Analyzing the Spread of COVID-19 in Turkey Using Long Short-Term Memory, In Proceedings of the 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 17–26, 2021.

[18] Chen Y., Liu F., Yu Q., Li T. Review of fractional epidemic models, Applied mathematical modelling, 97, 281-307, 2021.

[19] Sadria M., Layton A. T., Modeling within-host SARS-CoV-2 infection dynamics and potential treatments. Viruses, 13(6), 1141, 2021.

[20] Netea M. G., Giamarellos-Bourboulis E. J., Domínguez-Andrés J., Curtis N., van Crevel R., van de Veerdonk F. L.,  Bonten M.,Trained immunity: a tool for reducing susceptibility to and the severity of SARS-CoV-2 infection. Cell, 181(5), 969-977, 2020.

[21] Kaplan E. H., Wang D., Wang M., Malik A. A., Zulli A., Peccia J., Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge, Health care management science, 24, 320-329, 2021.

[22] Baral S. D., Mishra S., Diouf  D., Phanuphak N., Dowdy D.,  The public health response to COVID-19: balancing precaution and unintended consequences. Annals of epidemiology, 46, 12, 2020.

[23] Chen K., Pun C. S., Wong H. Y., Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations. European journal of operational research, 304(1), 84-98, 2023.

[24] Maziarz M., Zach M., Agent‐based modelling for SARS‐CoV‐2 epidemic prediction and intervention assessment: A methodological appraisal. Journal of Evaluation in Clinical Practice, 26(5), 1352-1360, 2020.

[25] Agarwal A., Mishra A., Sharma P., Jain S., Ranjan S., Manchanda R., Using LSTM for the Prediction of Disruption in ADITYA Tokamak, arXiv 2020, preprint. arXiv:2007.06230. 

[26] Abotaleb M.S., Makarovskikh T., Analysis of Neural Network and Statistical Models Used for Forecasting of a Disease Infection Cases. In Proceedings of the 2021 International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russia,1-7, 20–24 September 2021.

[27] Shahin A. I., Almotairi S., A deep learning BiLSTM encoding-decoding model for COVID-19 pandemic spread forecasting,  Fractal and Fractional, 5(4), 175, 2021.

[28] Wang T., Chen P., Rochford J., Qiang J., Text simplification using neural machine translation. In Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 30, 12–17, 2016.

[29] Du S., Li T., Yang Y., Horng S.J., Multivariate time series forecasting via attention-based encoder–decoder framework. Neurocomputing, 388, 269–279, 2020.

[30] Laubscher R., Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks. Energy, 189, 2019.

[31] Zhang B., Zou G., Qin D., Lu, Y., Jin Y., Wang H., A novel Encoder–Decoder model based on read-first LSTM for air pollutant prediction. Sci. Total Environ. , 765, 144507, 2021.

[32] Zerkouk M.,  Chikhaoui  B., Spatio-temporal abnormal behavior prediction in elderly persons using deep learning models, Sensors, 20, 2359, 2020. 

[33] Lyu P., Chen N., Mao S., Li M., LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion. Process. Saf. Environ. Prot. , 137, 93–105, 2020.

[34] Pham P., Pedrycz W., Vo B., Dual attention-based sequential auto-encoder for Covid-19 outbreak forecasting: A case study in Vietnam. Expert Systems with Applications, 203, 117514, 2022.

[35] Hochreiter  S., Hochreiter  J., Long short-term memory. Neural computation, 8(9), 1735-1780, 1977.

[36] Gers F., Eck D., Schmidhuber  J.,  Applying LSTM to time series predictable through time-window approaches. Neural Nets WIRN Vietri-01, 193-200, 2002.

[37] Huynh H., Dang L., Duong D.,  A new model for stock price movements prediction using deep neural network. In Proceedings of the Eighth International Symposium on Information and Communication Technology, 57-62, 2017.

[38] Zhang  X., Liang X., Zhiyuli A., Zhang S., Xu R., Cheng Z., & et al., AT-LSTM: An attention-based LSTM model for financial time series prediction. In IOP Conference Series: Materials Science and Engineering, 569 (5), 052037, 2019.

[39] Song X., Liu Y., Xue L., Wang J., Zhang J., Wang J., et al., Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, 186, 2020.

[40] Van Houdt G., Mosquera C., Nápoles G., & et al., A review on the long short-term memory model. Artif Intell Rev, 53, 5929–5955, 2020.

[41] Reddy D., & Prasad P., Prediction of vegetation dynamics using NDVI time series data and LSTM. Modeling Earth Systems and Environment, 4(1), 409-419, 2018.

[42] Moghar A., & Hamiche M., Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173, 2020.

[43] Rajagukguk R., Ramadhan R., & Lee H. A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power, Energies, 13(24), 6623, 2020.

[44] Zeydalinejad N., Artificial neural networks vis-à-vis MODFLOW in the simulation of groundwater: A review, Modeling Earth Systems and Environment, 1-22, 2022.

[45] Zhang B., Zou G., Qin D., Lu Y., Jin Y., & Wang H., A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction. Science of The Total Environment, 765, 144507, 2021.

[46] Park S., Kim B., Kang C., Chung C., & Choi J., Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. IEEE Intelligent Vehicles Symposium, 1672-1678, 2018.

[47] Ellis M., & Chinde V., An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system, Chemical Engineering Research and Design, 160, 508-520, 2020.

[48] Wang Y., Huang M., Zhu X., & Zhao L., Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing, 606-615, 2016.

[49] Kim S., & Kang M. (2019). Financial series prediction using Attention LSTM. arXiv preprint arXiv, 1902-10877.

[50] Liu G., & Guo J., Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 325-338, 2019.

[51] Biswas S., & Sinha M., Performances of deep learning models for Indian Ocean wind speed prediction, Modeling Earth Systems and Environment, 7(2), 809-831, 2021.

[52] Li Y., Zhu Z., Kong D., Han H., & Zhao Y., EA-LSTM: Evolutionary attention-based LSTM for time series prediction. Knowledge-Based Systems, 181, 104785, 2019.

[53] Xiao Y., Yin H., Zhang Y., Qi H., Zhang Y., & Liu Z., A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction. International Journal of Intelligent Systems, 36(5), 2036-2057, 2021.

 


Cite this Article as :
Style #
MLA Khder Alakkari , Alhumaima Ali Subhi, Hussein Alkattan, Ammar Kadi, Artem Malinin , Irina Potoroko , Mostafa Abotaleb , El-Sayed M El-kenawy. "Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms." Journal of Intelligent Systems and Internet of Things, Vol. 8, No. 2, 2023 ,PP. 20-33 (Doi   :  https://doi.org/10.54216/JISIoT.080202)
APA Khder Alakkari , Alhumaima Ali Subhi, Hussein Alkattan, Ammar Kadi, Artem Malinin , Irina Potoroko , Mostafa Abotaleb , El-Sayed M El-kenawy. (2023). Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms. Journal of Journal of Intelligent Systems and Internet of Things, 8 ( 2 ), 20-33 (Doi   :  https://doi.org/10.54216/JISIoT.080202)
Chicago Khder Alakkari , Alhumaima Ali Subhi, Hussein Alkattan, Ammar Kadi, Artem Malinin , Irina Potoroko , Mostafa Abotaleb , El-Sayed M El-kenawy. "Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms." Journal of Journal of Intelligent Systems and Internet of Things, 8 no. 2 (2023): 20-33 (Doi   :  https://doi.org/10.54216/JISIoT.080202)
Harvard Khder Alakkari , Alhumaima Ali Subhi, Hussein Alkattan, Ammar Kadi, Artem Malinin , Irina Potoroko , Mostafa Abotaleb , El-Sayed M El-kenawy. (2023). Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms. Journal of Journal of Intelligent Systems and Internet of Things, 8 ( 2 ), 20-33 (Doi   :  https://doi.org/10.54216/JISIoT.080202)
Vancouver Khder Alakkari , Alhumaima Ali Subhi, Hussein Alkattan, Ammar Kadi, Artem Malinin , Irina Potoroko , Mostafa Abotaleb , El-Sayed M El-kenawy. Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 8 ( 2 ): 20-33 (Doi   :  https://doi.org/10.54216/JISIoT.080202)
IEEE Khder Alakkari, Alhumaima Ali Subhi, Hussein Alkattan, Ammar Kadi, Artem Malinin, Irina Potoroko, Mostafa Abotaleb, El-Sayed M El-kenawy, Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 2 , (2023) : 20-33 (Doi   :  https://doi.org/10.54216/JISIoT.080202)