IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p1051-d1073444.html
   My bibliography  Save this article

Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19

Author

Listed:
  • Ahed Abugabah

    (College of Technological Innovation, Abu Dhabi Campus, Zayed University, FF2-0-056, Abu Dhabi P.O. Box 144534, United Arab Emirates)

  • Farah Shahid

    (Department of Computer Sciences, Sub Campus Burewala-Vehari, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan)

Abstract

The rapidly growing number of COVID-19 infected and death cases has had a catastrophic worldwide impact. As a case study, the total number of death cases in Algeria is over two thousand people (increased with time), which drives us to search its possible trend for early warning and control. In this paper, the proposed model for making a time-series forecast for daily and total infected cases, death cases, and recovered cases for the countrywide Algeria COVID-19 dataset is a two-layer dropout gated recurrent unit (TDGRU). Four performance parameters were used to assess the model’s performance: mean absolute error (MAE), root mean squared error (RMSE), R 2 , and mean absolute percentage error (MAPE). The results generated with TDGRU are compared with actual numbers as well as predictions with conventional time-series techniques, such as autoregressive integrated moving average (ARIMA), machine learning model of linear regression (LR), and the time series-based deep learning method of long short-term memory (LSTM). The experiment results on different time horizons show that the TDGRU model outperforms the other forecasting methods that deliver correct predictions with lower prediction errors. Furthermore, since this TDGRU is based on a relatively simpler architecture than the LSTM, in comparison to LSTM-based models, it features a significantly reduced number of parameters, a shorter training period, a lower memory storage need, and a more straightforward hardware implementation.

Suggested Citation

  • Ahed Abugabah & Farah Shahid, 2023. "Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1051-:d:1073444
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/1051/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/4/1051/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Arora, Parul & Kumar, Himanshu & Panigrahi, Bijaya Ketan, 2020. "Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Mohamed R Ibrahim & James Haworth & Aldo Lipani & Nilufer Aslam & Tao Cheng & Nicola Christie, 2021. "Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-22, January.
    4. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    5. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    6. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    7. Khan, Firdos & Saeed, Alia & Ali, Shaukat, 2020. "Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Comparison of deep learning approaches to predict COVID-19 infection," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    9. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    10. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    11. Shastri, Sourabh & Singh, Kuljeet & Kumar, Sachin & Kour, Paramjit & Mansotra, Vibhakar, 2020. "Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Ola Karajeh & Dirar Darweesh & Omar Darwish & Noor Abu-El-Rub & Belal Alsinglawi & Nasser Alsaedi, 2021. "A Classifier to Detect Informational vs. Non-Informational Heart Attack Tweets," Future Internet, MDPI, vol. 13(1), pages 1-10, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    2. Shruti Sharma & Yogesh Kumar Gupta & Abhinava K. Mishra, 2023. "Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods," IJERPH, MDPI, vol. 20(11), pages 1-23, May.
    3. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Schaum, A. & Bernal-Jaquez, R. & Alarcon Ramos, L., 2022. "Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    5. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    6. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    7. Yong-Ju Jang & Min-Seung Kim & Chan-Ho Lee & Ji-Hye Choi & Jeong-Hee Lee & Sun-Hong Lee & Tae-Eung Sung, 2022. "A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-22, June.
    8. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    9. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    10. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    11. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Middya, Asif Iqbal & Roy, Sarbani, 2022. "Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    13. Ali, Furqan & Ullah, Farman & Khan, Junaid Iqbal & Khan, Jebran & Sardar, Abdul Wasay & Lee, Sungchang, 2023. "COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    14. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    15. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    16. Crokidakis, Nuno, 2020. "COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    17. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    18. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    19. Caraballo, T. & Settati, A. & Lahrouz, A. & Boutouil, S. & Harchaoui, B., 2024. "On the stochastic threshold of the COVID-19 epidemic model incorporating jump perturbations," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    20. Abbasimehr, Hossein & Paki, Reza, 2021. "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1051-:d:1073444. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.