COVID-19: An Efficient Big Data Analytics for SARS-CoV-2 Mutations Prediction: A Machine Learning Approach

Dr  Saeed Q Al Khalidi Al Maliki; Dr  Prakash Kuppuswamy; Dr  Rajan John; Dr  Nithya Rekha Sivakumar

doi:10.17485/IJST/v15i21.374

Article

COVID-19: An Efficient Big Data Analytics for SARS-CoV-2 Mutations Prediction: A Machine Learning Approach

VIEWS 1216
PDF 172

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i21.374

Year: 2022, Volume: 15, Issue: 21, Pages: 1022-1031

Original Article

COVID-19: An Efficient Big Data Analytics for SARS-CoV-2 Mutations Prediction: A Machine Learning Approach

Dr. Saeed Q Al-Khalidi Al-Maliki^1*, Dr. Prakash Kuppuswamy^2*, Dr. Rajan John³, Dr. Nithya Rekha Sivakumar⁴

¹Department of MIS, College of Business, King Khalid University, Abha, KSA
²Computer Networks Engineering Department, College of CS & IT, Jazan University, Jazan, KSA
³Department of Computer Science & Engineering, College of CS & IT, Jazan University, Jazan, KSA
⁴Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

*Corresponding Author
Email: [email protected]
[email protected]

Received Date:15 February 2022, Accepted Date:23 April 2022, Published Date:09 June 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This research article aims to identify the level of further mutations of COVID-19 and its vulnerability. Healthcare workers are urged to take precautionary measures to prevent further damage to their community and to avoid further human deaths. At this stage, it is crucial to study, analyze, and understand the COVID-19 variant mutation. Methods: The mutation of COVID- 19, however, has not yet been validated for the way in which it affects the risk of underlying comorbidities. It may be possible to tackle this challenge by using artificial intelligence, machine learning, and deep learning algorithms. We have used the J48 algorithm and the Linear Regression algorithm. These algorithms were then analyzed based on the accuracy they provided after running them in the output window of WEKA data mining. Findings: In this paper, machine learning is used to help clinicians and medical researchers understand COVID- 19 variant mutations at various stages. Artificial intelligence-based algorithms provide a better understanding of COVID-19 stages and vulnerability levels. Novelty: Artificial intelligence can be a valuable tool for healthcare operations in this pandemic situation. Moreover, healthcare professionals and a data analysis algorithm will probably arrive at the same conclusion based on the data set. However, the use of machine learning-based techniques described in this article will allow for quicker and earlier diagnosis of any type of pandemic situation in the future.

Keywords: COVID19; Machine Learning; Mutation; Variant; Coronavirus; SARSCoV2; Prediction algorithm etc

References

Alga A, Eriksson O, Nordberg M. Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study”. Journal of Medical Internet Research. 2020;22(11). Available from: https://doi.org/10.2196/21559
Alexandra L, Katz R, Lawrence O. The novel coronavirus originating in Wuhan, China: challenges for global health governance”. Pub. Med, National Library of Science. 2020;8. Available from: https://doi.org/10.3390/healthcare10020299
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. New England Journal of Medicine. 2020;382(13):1199–1207. Available from: https://doi.org/ 10.1056/NEJMoa2001316
Word Health Organization - Regional Office for Europe. Copenhagen, Denmark . Available from: http://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-COVID-19/news/news/2020/3/who-announces-COVID-19-outbreak-a-pandemic (accessed 2021-12-25)
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020;28:30566–30569. Available from: https://doi.org/ 10.1016/S0140-6736(20)30566-3
Thorlund K, Dron L, Park J, Hsu G, Forrest JI, Mills EJ. A real-time dashboard of clinical trials for COVID-19”. Lancet Digital Health. 2020;6:286–287. Available from: https://doi.org/ 10.3390/ijerph17114095
Chadaga K, Prabhu S, Vivekananda BK, Niranjana S, Umakanth S. Battling COVID-19 using machine learning: A review. Cogent Engineering. 2021;8(1). Available from: https://doi.org/10.1080/23311916.2021.1958666
Global Coronavirus COVID-19 Clinical Trial Tracker. Global Coronavirus COVID-19 Clinical Trial Tracker. Available from: https://www.covid-trials.org/ (accessed 2021-12-26)
Choi TM. Risk analysis in logistics systems: A research agenda during and after the COVID-19 pandemic. Transportation Research Part E: Logistics and Transportation Review. 2021;145:102190. Available from: https://doi.org/10.1016/j.tre.2020.102190
Ivanov D. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review. 2020;136:101922. Available from: https://doi.org/10.1016/j.tre.2020.101922
Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, et al. Coronavirus Pandemic (COVID-19) 2020. Available from: https://ourworldindata.org/covid-cases
Chen N, Zhou M, Dong X. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:30211–30218. Available from: https://doi.org/10.1016/S0140-6736(20)30211-7
Young BE, Ong SWX, Kalimuddin S, Low JG, Tan SY, Loh J, et al. Epidemiologic Features and Clinical Course of Patients Infected With SARS-CoV-2 in Singapore. JAMA. 2020;323(15):1488. Available from: https://doi.org/doi:10.1001/jama.2020.3204
Huang C, Wang Y, Li X. Clinical features of patients infected with 2019 novel coronavirus in Wuhan. Lancet. 2020;(20) 30183–30188. Available from: https://doi.org/10.1016/ S0140-6736(20)30183-5
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA. 2020;323(11):1061. Available from: https://doi.org/10.1001/jama.2020.1585
Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine. 2020;382(18):1708–1720. Available from: https://doi.org/10.1056/NEJMoa2002032
Arabi YM, Shalhoub S, Mandourah Y, Al-Hameed F, Al-Omari A, Qasim EA, et al. Ribavirin and Interferon Therapy for Critically Ill Patients With Middle East Respiratory Syndrome: A Multicenter Observational Study. Clinical Infectious Diseases. 2020;70(9):1837–1844. Available from: https://doi.org/10.1093/cid/ciz544
Kumar A, Gupta P, Srivastava A. A review of modern technologies for tackling COVID-19 pandemic, Diabetes and Metabolic Syndrome. Clinical Research and Reviews. 2020;14(4). Available from: https://doi.org/ 10.1016/j.dsx.2020.05.008
Kobayashi T, Jung SM, Linton NM, Kinoshita R, Hayashi K, Miyama T, et al. Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19) Journal of Clinical Medicine. 2020;9(2):580. Available from: https://doi.org/10.3390/jcm9020580
Page ML. Stopping corona Virus. New Scientist. 2021;252:3365–3366. Available from: https://www.sciencedirect.com/journal/new-scientist/vol/252/issue/3365
Wang LL, Lo K, Chandrasekhar Y, Reas R, Yang J, Eide D, et al. CORD-19: The COVID-19 Open Research Dataset”, PMC Lab, US National Library of Medicine National Institutes of Health. 2020. Available from: https://pubmed.ncbi.nlm.nih.gov/32510522/
Lopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo PA, Cuapio A, et al. More than 50 Long-term effects of COVID-19: a systematic review and meta-analysis. medRxiv. 2021. Available from: https://doi.org/10.1101/2021.01.27.21250617
Bukhari SNH, Jain A, Haq E, Mehbodniya A, Webber J. Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review. Pathogens. 2022;11(2):146. Available from: https://doi.org/10.3390/pathogens11020146
Tan L, Wang Q, Zhang D, Qianchuan JD, Huang YQ, Tang Q, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduction and Targeted Therapy. 2020;5(33). Available from: https://doi.org/10.1038/s41392-020-0148-4
Magazine, Centers for Disease control and prevention, United states. Available from: https://covid.cdc.gov/covid-data-tracker/#variant-proportionsaccessedon9/1/2022

Copyright

© 2022 Al-Maliki 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.

Published By Indian Society for Education and Environment (iSee)