IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i19p7080-d420613.html
   My bibliography  Save this article

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Author

Listed:
  • Abdelrahman E. E. Eltoukhy

    (Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Ibrahim Abdelfadeel Shaban

    (Faculty of Engineering, Helwan University, Helwan 11795, Egypt)

  • Felix T. S. Chan

    (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

  • Mohammad A. M. Abdel-Aal

    (Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

Suggested Citation

  • Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & Felix T. S. Chan & Mohammad A. M. Abdel-Aal, 2020. "Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations," IJERPH, MDPI, vol. 17(19), pages 1-25, September.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7080-:d:420613
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/19/7080/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/19/7080/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    2. Lei Qin & Qiang Sun & Yidan Wang & Ke-Fei Wu & Mingchih Chen & Ben-Chang Shia & Szu-Yuan Wu, 2020. "Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index," IJERPH, MDPI, vol. 17(7), pages 1-14, March.
    3. Wieczorek, Michał & Siłka, Jakub & Woźniak, Marcin, 2020. "Neural network powered COVID-19 spread forecasting model," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    5. Misiunas, Nicholas & Oztekin, Asil & Chen, Yao & Chandra, Kavitha, 2016. "DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status," Omega, Elsevier, vol. 58(C), pages 46-54.
    6. Dabiah Alboaneen & Bernardi Pranggono & Dhahi Alshammari & Nourah Alqahtani & Raja Alyaffer, 2020. "Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia," IJERPH, MDPI, vol. 17(12), pages 1-10, June.
    7. Eltoukhy, Abdelrahman E.E. & Wang, Z.X. & Chan, Felix T.S. & Fu, X., 2019. "Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 143-168.
    8. Sai Ho Chung & Hoi Lam Ma & Hing Kai Chan, 2017. "Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1443-1458, August.
    9. Nicola Luigi Bragazzi & Haijiang Dai & Giovanni Damiani & Masoud Behzadifar & Mariano Martini & Jianhong Wu, 2020. "How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic," IJERPH, MDPI, vol. 17(9), pages 1-8, May.
    10. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
    11. Hashim. A. Hashim & Sami El-Ferik & Frank L. Lewis, 2017. "Adaptive synchronisation of unknown nonlinear networked systems with prescribed performance," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(4), pages 885-898, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sariyer, Gorkem & Mangla, Sachin Kumar & Kazancoglu, Yigit & Jain, Vranda & Ataman, Mustafa Gokalp, 2023. "Data-driven decision making for modelling covid-19 and its implications: A cross-country study," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    2. Ahmed Karam & Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & El-Awady Attia, 2022. "A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies, Recovery Measures, and Future Research Directions," IJERPH, MDPI, vol. 19(19), pages 1-27, September.
    3. Diego Galvan & Luciane Effting & Hágata Cremasco & Carlos Adam Conte-Junior, 2020. "Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?," IJERPH, MDPI, vol. 17(23), pages 1-16, November.

    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. Anshuman Satapathy & Niranjan Nayak & Tanmoy Parida, 2022. "Real-Time Power Quality Enhancement in a Hybrid Micro-Grid Using Nonlinear Autoregressive Neural Network," Energies, MDPI, vol. 15(23), pages 1-35, November.
    2. Eltoukhy, Abdelrahman E.E. & Wang, Z.X. & Chan, Felix T.S. & Fu, X., 2019. "Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 143-168.
    3. Israel Edem Agbehadji & Bankole Osita Awuzie & Alfred Beati Ngowi & Richard C. Millham, 2020. "Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing," IJERPH, MDPI, vol. 17(15), pages 1-16, July.
    4. Guo, Bowei & Newbery, David, 2021. "The cost of uncoupling GB interconnectors," Energy Policy, Elsevier, vol. 158(C).
    5. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    6. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training," Applied Energy, Elsevier, vol. 355(C).
    7. 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.
    8. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    9. Timothy Praditia & Thilo Walser & Sergey Oladyshkin & Wolfgang Nowak, 2020. "Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture," Energies, MDPI, vol. 13(15), pages 1-26, July.
    10. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    11. Francisco Javier Santos Arteaga & Debora Di Caprio & David Cucchiari & Josep M Campistol & Federico Oppenheimer & Fritz Diekmann & Ignacio Revuelta, 2021. "Modeling patients as decision making units: evaluating the efficiency of kidney transplantation through data envelopment analysis," Health Care Management Science, Springer, vol. 24(1), pages 55-71, March.
    12. Al-Ebbini, Lina & Oztekin, Asil & Chen, Yao, 2016. "FLAS: Fuzzy lung allocation system for US-based transplantations," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1051-1065.
    13. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    14. Oksana Mandrikova & Yuryi Polozov & Nataly Zhukova & Yulia Shichkina, 2022. "Approximation and Analysis of Natural Data Based on NARX Neural Networks Involving Wavelet Filtering," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    15. Yas Al-Hadeethi & Intesar F. El Ramley & Hiba Mohammed & Nada M. Bedaiwi & Abeer Z. Barasheed, 2024. "A Novel Computational Instrument Based on a Universal Mixture Density Network with a Gaussian Mixture Model as a Backbone for Predicting COVID-19 Variants’ Distributions," Mathematics, MDPI, vol. 12(8), pages 1-24, April.
    16. Yongzhu Xiong & Yunpeng Wang & Feng Chen & Mingyong Zhu, 2020. "Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China," IJERPH, MDPI, vol. 17(11), pages 1-26, May.
    17. Guilherme Henrique Alves & Geraldo Caixeta Guimarães & Fabricio Augusto Matheus Moura, 2023. "Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing," Energies, MDPI, vol. 16(14), pages 1-30, July.
    18. Fjelkestam Frederiksen, Cornelia A. & Cai, Zuansi, 2022. "Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance," Applied Energy, Elsevier, vol. 306(PB).
    19. Alexander E. Kentikelenis & Leonard Seabrooke, 2022. "Governing and Measuring Health Security: The Global Push for Pandemic Preparedness Indicators," Global Policy, London School of Economics and Political Science, vol. 13(4), pages 571-578, September.
    20. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.

    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:jijerp:v:17:y:2020:i:19:p:7080-:d:420613. 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.