Abderrahmane Ez-zahout¹*, Slimane El Ouafi² and Omar Aitoulghazi²

¹IPSS Team, Faculty of Science, Mohamed V University, Morocco
²School of Science and Engineering, Al Akhawayn University, Morocco

*Corresponding Author: Abderrahmane Ez-zahout, IPSS Team, Faculty of Science, Mohamed V University, Morocco.

Received: June 25, 2021; Published: September 07, 2021

Abstract

  The COVID-19 pandemic has caused a large number of human losses and impacted different sectors; economic, social, societal, and health systems around the world. Controlling such pandemic requires understanding its characteristics, causes and consequences, which can be done using data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, to do that, many challenges are faced whether it is about privacy and security, data sharing, information correctness, or patient cooperation. To better understand how this pandemic is affecting our world and the countries that got the most affected, we did some analyzed data that we extracted from a free provider and visualized.

Keywords: COVID-19; Big Data Analytics; Making Decision; Patterns; Knowledge

Bibliography

1. Paul Blake Divyanshi Wadhwa. “2020 Year in Review: The impact of COVID-19 in 12 charts”. World Bank Blogs (2020).
2. , et al. “Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing”. International Journal of Environmental Research and Public Health 17.15 (2020): 5330.
3. Alsunaidi S J., et al. “Applications of Big Data Analytics to Control COVID-19 Pandemic”. Sensors 7 (2021): 2282.
4. Ardakani AA., et al. “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks”. Computers in Biology and Medicine 121 (2020): 103795.
5. Benreguia B., et al. “Tracking COVID-19 by Tracking Infectious Trajectories”. IEEE Access 8 (2020): 145242-145255.
6. Brown C., et al. “Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data”. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2020. Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data (ACM), New York, NY, USA, 25-27 August (2020): 3474-3484.
7. Chen Q., et al. “Cardiovascular Manifestations in Severe and Critical Patients with COVID -19”. Clinical Cardiology 43 (2020): 796-802.
8. Khan MA., et al. “A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network”. Symmetry 10 (2018): 485.
9. Lalmuanawma S., et al. “Applications of Machine Learning and Artificial Intelligence for Covid-19 (SARS-CoV-2) pandemic: A review”. Chaos, Solitons and Fractals (2020): 110059.
10. Ozturk T., et al. “Automated detection of COVID-19 cases using deep neural networks with X-ray images”. Computers in Biology and Medicine 121 (2020): 103792.
11. PEX Process Excellence Network 6 Ways Pharmaceutical Companies are Using Big Data to Drive Innovation and Value (2020).
12. Sheng Jie., et al. “COVID-19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions”. British Journal of Management (2021).
13. Sun L., et al. “Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19”. Journal of Clinical Virology (2020): 104431.
14. Wu J., et al. “Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results”. medRxiv (2020).

Citation

Citation: Abderrahmane Ez-zahout., et al. “A Real Time Analytics for COVID-19 Tracking Based on Big Data Extracted on a Free Online Provider". Acta Scientific Computer Sciences 3.10 (2021): 27-32.

Copyright

Copyright: © 2021 Abderrahmane Ez-zahout., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.