verifiedOpen Access Peer-Reviewed Scientific Journal (CC BY 4.0)
Submit Manuscript|OJS Login|Register
Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
search
person
Research ArticlesverifiedPeer-ReviewedOpen Access
Pages: 1-10

Scientometric Analysis of COVID-19 Scholars Publication using Machine Learning

David OYEWOLA fingerprint•
Emmanuel DADA fingerprint
Publication DateMarch 31, 2022
Volume / IssueVol. 10, No. 1 (pp. 1-10)
subject

Abstract

The global health crisis that started in December 2019 resulted in an outbreak of coronavirus named COVID-19. Scientists worldwide are working to demystify the transmission and pathogenic mechanisms of the deadly coronavirus. The World Health Organization has declared COVID-19 a pandemic in March 2020, which makes it essential to track and analyse the research state of COVID-19 for guidance on further research. This research was conducted using scientometric analysis, knowledge-mapping analysis, COVID-19 studies and journal classifications. The publications used in this study include over 3000 COVID-19 papers made available to the public from 1 January 2018 to 15 April 2021 in the PubMed databases. In this study, it was discovered that the rapid reaction of researchers worldwide resulted in a fast growth trend between 2019 and 2021 in the number of publications related to COVID-19. It was discovered that the largest number of studies is in the United States of America, which is one of the countries most affected by a pandemic. The method adopted for this study involved the use of documents such as Case Reports (CAT), Journal Article (JAT), letter (LTR), EAT, and Editorial (EDT). This is followed by the classification of COVID-19 related publications that were retrieved from PubMed between 2019 and 2021 using machine learning (ML) models such as Naïve Bayes (NB), Bayesian Generalized Linear Model (BGL), Heteroscedastic Discriminant Analysis (HDA) and Multivariate Adaptive Regression Spline (MAR). Simulation results show that the classification accuracy of MAR is better than that of other ML models used in this study. The sensitivity of the MAR is within the range of 100%. This shows that MAR performs better than NB, BGL and HDA. MAR performs better with an overall accuracy of 89.62%. Our results show a high degree of strong collaboration in coronavirus research and the exchange of knowledge in the global scientific community.
Keywords:Bayesian Generalized Linear ModelHeteroscedastic Discriminant AnalysisMultivariate Adaptive Regression SplineNaïve BayesScientometric

Author Affiliations

format_list_numbered

References (49)

Cited Literature
  1. 1

    WHO, “Novel Coronavirus (2019-nCoV) Situation Report-1”, World Health Organization. Geneva, Switzerland; 2020. Available at: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf?sfvrsn=20a99c10_4 [Accessed June 2021].

  2. 2

    ILO, FAO, IFAD and WHO, "Impact of COVID-19 on people's livelihoods, their health and our food systems"; 2020. Available at: https://www.who.int/news/item/13-10-2020-impact-of-covid-19-on-people%27s-livelihoods-their-health-and-our-food-systems [Accessed May 2021].

  3. 3

    Worldometer, “COVID-19 Coronavirus Pandemic”; 2021. Available at: https://COVID Live Update: 142,813,353 Cases and 3,046,229 Deaths from the Coronavirus - Worldometer (worldometers.info) [Accessed May 2021].

  4. 4

    P. Yang, X. Wang, "COVID-19: a new challenge for human beings", Cellular & molecular immunology, vol. 17, no. 5, pp. 555-557, 2020.

  5. 5

    A. Aristovnik, D. Ravšelj, L. Umek, “A bibliometric analysis of COVID-19 across science and social science research landscape”, Sustainability, vol. 12, no. 21, pp. 9132, 2020.

  6. 6

    B. Xie, D. He, T. Mercer, Y. Wang, D. Wu, K. R. Fleischmann, Y. Zhang, L. H. Yoder, K. K. Stephens, M. Mackert, M. K. Lee, “Global health crises are also information crises: A call to action”, Journal of the Association for Information Science and Technology, vol. 71, no. 12, pp. 1419-23, 2020.

  7. 7

    M. Cinelli, W. Quattrociocchi, A. Galeazzi, C. M. Valensise, E. Brugnoli, A. L. Schmidt, P. Zola, F. Zollo, A. Scala, “The COVID-19 social media infodemic”, Scientific Reports, vol. 10, no. 1, pp. 1-10, 2020.

  8. 8

    B. Swire-Thompson, D. Lazer, “Public health and online misinformation: Challenges and Recommendations”, Annual Review of Public Health, vol. 41, no. 1, pp. 433–451, 2020. https://doi.org/10.1146/annurevpublhealth-040119-094127 PMID: 31874069

    open_in_newCrossref / DOI: 10.1146/annurevpublhealth-040119-094127
  9. 9

    J. P. Ioannidis, “Coronavirus disease 2019: the harms of exaggerated information and non‐evidence‐based measures”, European Journal of clinical investigation, vol. 50, no. 4, 2020.

  10. 10

    J. Zarocostas, “How to fight an infodemic”, Lancet. 395(10225), 2020. https://doi.org/10.1016/S0140-6736(20)30461-X.

    open_in_newCrossref / DOI: 10.1016/S0140-6736(20)30461-X
  11. 11

    EPI-WIN “WHO Information Network for Epidemics”, 2020; Available at: https://www.who.int/teams/risk-communication.

  12. 12

    L. L. Wang, K. Lo, Y. Chandrasekhar, R. Reas, J. Yang, D. Eide, K. Funk, R. Kinney, Z. Liu, W. Merrill, P. Mooney, “Cord-19: The covid-19 open research dataset”, ArXiv. Jul 9, 2020.

  13. 13

    A. Doanvo, X. Qian, D. Ramjee, H. Piontkivska, A. Desai, M. Majumder, “Machine learning maps research needs in covid-19 literature”, Patterns, 1(9):100123, Dec 11, 2020.

  14. 14

    A. Aristovnik, D. Ravšelj, L. Umek, “A bibliometric analysis of COVID-19 across science and social science research landscape”, Sustainability, 12(21):9132, Jan. 2020 [doi: 10.20944/preprints202006.0299.v1]

    open_in_newCrossref / DOI: 10.20944/preprints202006.0299.v1
  15. 15

    M. Haghani, M. C. Bliemer, F. Goerlandt, J. Li, “The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review”, Safety Science, 1;129:104806, 2020 [doi: 10.1016/j.ssci.2020.104806]

    open_in_newCrossref / DOI: 10.1016/j.ssci.2020.104806
  16. 16

    A. Doanvo, X. Qian, D. Ramjee, H. Piontkivska, A. Desai, M. Majumder, “Machine learning maps research needs in covid-19 literature”, Patterns, 1(9):100123, 2020. [doi: 10.1101/2020.06.11.145425]

    open_in_newCrossref / DOI: 10.1101/2020.06.11.145425
  17. 17

    M. Dong, X. Cao, M. Liang, L. Li, H. Liang, G. Liu, "Understand research hotspots surrounding COVID-19 and other coronavirus infections using topic modelling", medRxiv. Jan 2020. [doi: 10.1101/2020.03.26.20044164]

    open_in_newCrossref / DOI: 10.1101/2020.03.26.20044164
  18. 18

    P. Le Bras, A. Gharavi, D. A. Robb, A. F. Vidal, S. Padilla, M. J. Chantler, “Visualising COVID-19 research”, arXiv preprint, 2020.

  19. 19

    X. Mao, L. Guo, P. Fu, C. Xiang, “The status and trends of coronavirus research: A global bibliometric and visualized analysis”, Medicine, 29;99(22):e20137, 2020.

  20. 20

    A. Abd-Alrazaq, J. Schneider, B. Mifsud, T. Alam, M. Househ, M. Hamdi, Z. Shah, “A comprehensive overview of the COVID-19 literature: Machine learning-based bibliometric analysis”, Journal of medical Internet research, 8;23(3):e23703, 2021.

  21. 21

    G. Colavizza, R. Costas, V. A. Traag, N. J. Van Eck, T. Van Leeuwen, L. Waltman, “A scientometric overview of CORD-19”, PloS one, 7;16(1):e0244839, 2021. https://doi.org/10.1371/journal.pone.0244839

    open_in_newCrossref / DOI: 10.1371/journal.pone.0244839
  22. 22

    NIH, “National Library of Medicine”, National Centre for Biotechnology Information, 2021; Available from: https://pubmed.ncbi.nih.gov.

  23. 23

    Y. Gong, T. C. Ma, Y. Y. Xu, R. Yang, L. J. Gao, S. H. Wu, J. Li, M. L. Yue, H. G. Liang, X. He, T. Yun, “Early research on COVID-19: a bibliometric analysis”, The Innovation, 1(2):100027, Aug 28, 2020. https://doi.org/10.1016/j.xinn.2020.100027.

    open_in_newCrossref / DOI: 10.1016/j.xinn.2020.100027
  24. 24

    F. De Felice, A. Polimeni, “Coronavirus disease (COVID-19): a machine learning bibliometric analysis”, in vivo, 34(3 suppl), 1613-1617, 2020.

  25. 25

    F. R. Nasab, "Bibliometric analysis of global scientific research on SARS-CoV-2 (Covid-19)", MedRxiv. Jan 1, 2020.

  26. 26

    H. Dehghanbanadaki, F. Seif, Y. Vahidi, F. Razi, E. Hashemi, M. Khoshmirsafa, H. Aazami, “Bibliometric analysis of global scientific research on Coronavirus (COVID-19)”, Medical Journal of the Islamic Republic of Iran, 34:51, 2020.

  27. 27

    M. Aria, C. Cuccurullo, “Bibliometrix: An R-tool for comprehensive science mapping analysis”, Journal of informetrics, 1;11(4):959-75, Nov 2017. https://doi.org/10.1016/j.joi.2017.08.007

    open_in_newCrossref / DOI: 10.1016/j.joi.2017.08.007
  28. 28

    R Package, 2021. Available at: www.bibliometrix.org [Accessed January 20, 2020].

  29. 29

    E. Alpaydin, Introduction to machine learning, MIT press; 2020 Mar 17.

  30. 30

    S. Kumar, S. Kumar, "Collaboration in research productivity in oilseed research institutes of India", InProceedings of Fourth International Conference on Webometrics, Informetrics and Scientometrics, Vol. 28, Jul 28, 2008.

  31. 31

    K. Yuan, L. Gao, Z. Jiang, Z. Tang, “Formula Ranking within an Article”, InProceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, pp. 123-126, May 23, 2018. doi:10.1145/3197026.3197061.

    open_in_newCrossref / DOI: 10.1145/3197026.3197061
  32. 32

    M. J. Sánchez-Franco, A. Navarro-García, F. J. Rondán-Cataluña, “A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services”, Journal of Business Research, 101:499-506, Aug 1, 2019. doi:10.1016/j.jbusres.2018.12.051.

    open_in_newCrossref / DOI: 10.1016/j.jbusres.2018.12.051
  33. 33

    G. Shi, C. Y. Lim, T. Maiti, “Bayesian model selection for generalized linear models using non-local priors”, Computational Statistics & Data Analysis, 133:285-96, May 1, 2019. doi:10.1016/j.csda.2018.10.007.

    open_in_newCrossref / DOI: 10.1016/j.csda.2018.10.007
  34. 34

    C. Gao, Q. Li, Z. Guo, “Automobile Insurance Pricing with Bayesian General Linear Model”, In International Conference on Information and Management Engineering, pp. 359-365, Sep 17, 2011, Springer, Berlin, Heidelberg.

  35. 35

    K. S. Gyamfi, J. Brusey, A. Hunt, E. Gaura, “Linear classifier design under heteroscedasticity in Linear Discriminant Analysis”, Expert Systems with Applications, 79:44-52, 2017 Aug 15. doi:10.1016/j.eswa.2017.02.039.

    open_in_newCrossref / DOI: 10.1016/j.eswa.2017.02.039
  36. 36

    K. Stąpor, T. Smolarczyk, P. Fabian, “Heteroscedastic discriminant analysis combined with feature selection for credit scoring”, Statistics in Transition new series, 17(2):265-80, 2016.

  37. 37

    M. Samadi, M. H. Afshar, E. Jabbari, H. Sarkardeh, “Application of multivariate adaptive regression splines and classification and regression trees to estimate wave-induced scour depth around pile groups”, Iranian Journal of Science and Technology, Transactions of Civil Engineering, 44(1):447-59, Oct. 2020. https://doi.org/10.1007/s40996-020-00364-2.

    open_in_newCrossref / DOI: 10.1007/s40996-020-00364-2
  38. 38

    D. O. Oyewola, A. F. Augustine, E. G. Dada, A. Ibrahim, “Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolution Neural Network”, Journal of Robotics and Control (JRC), 2(2):103-109, Mar 19, 2021.

  39. 39

    B. Chen, J. Han, H. Dai, P. Jia, “Biocide-tolerance and antibiotic-resistance in community environments and risk of direct transfers to humans: Unintended consequences of community-wide surface disinfecting during COVID-19”, Environmental Pollution, 117074, 2021 Apr 3. DOI: 10.1016/j.envpol.2021.117074.

    open_in_newCrossref / DOI: 10.1016/j.envpol.2021.117074
  40. 40

    N. Zaki, E. A. Mohamed, “The estimations of the COVID-19 incubation period: A scoping reviews of the literature”, Journal of infection and public health, 14(5):638-46, May 2021, DOI: 10.1016/j.jiph.2021.01.019

    open_in_newCrossref / DOI: 10.1016/j.jiph.2021.01.019
  41. 41

    Z. Chen, W. Xie, Z. Ge, Y. Wang, H. Zhao, J. Wang, Y. Xu, W. Zhang, M. Song, S. Cui, X. Wang, “Reactivation of SARS-CoV-2 infection following recovery from COVID-19”, Journal of infection and public health, 14(5):620-627, May 2021.

  42. 42

    S. Panikar, G. Shoba, M. Arun, J. J. Sahayarayan, A. U. Nanthini, A. Chinnathambi, S. A. Alharbi, O. Nasif, H. J. Kim, “Essential oils as an effective alternative for the treatment of COVID-19: Molecular interaction analysis of protease (Mpro) with pharmacokinetics and toxicological properties”, Journal of Infection and Public Health, 14(5):601-10, May 2021. DOI: 10.1016/j.jiph.2020.12.037.

    open_in_newCrossref / DOI: 10.1016/j.jiph.2020.12.037
  43. 43

    M. Chedid, R. Waked, E. Haddad, N. Chetata, G. Saliba, J. Choucair, “Antibiotics in treatment of COVID-19 complications: a review of frequency, indications, and efficacy”, Journal of infection and public health, 14(5):570, May 2021, DOI: 10.1016/j.jiph.2021.02.001.

    open_in_newCrossref / DOI: 10.1016/j.jiph.2021.02.001
  44. 44

    M. M. Khodeir, H. A. Shabana, A. S. Alkhamiss, Z. Rasheed, M. Alsoghair, S. A. Alsagaby SA, Khan MI, Fernández N, Al Abdulmonem W. Early prediction keys for COVID-19 cases progression: A meta-analysis. Journal of infection and public health. 2021 Mar 5, DOI: 10.1016/j.jiph.2021.03.001.

    open_in_newCrossref / DOI: 10.1016/j.jiph.2021.03.001
  45. 45

    H. Kitajima, Y. Tamura, H. Yoshida, H. Kinoshita, H. Katsuta, C. Matsui, A. Matsushita, T. Arai, S. Hashimoto, A. Iuchi, T. Hirashima, “Clinical COVID-19 diagnostic methods: Comparison of reverse transcription loop-mediated isothermal amplification (RT-LAMP) and quantitative RT-PCR (qRT-PCR)”, Journal of Clinical Virology, 1;139:104813, Jun 2021. DOI: 10.1016/j.jcv.2021.104813.

    open_in_newCrossref / DOI: 10.1016/j.jcv.2021.104813
  46. 46

    D. Dourado, D. T. Freire, D. T. Pereira, L. Amaral-Machado, E. N. Alencar, A. L. de Barros, E. S. Egito, “Will curcumin nanosystems be the next promising antiviral alternatives in COVID-19 treatment trials?” Biomedicine & Pharmacotherapy, 6:111578, Apr. 2021. DOI: 10.1016/j.biopha.2021.111578.

    open_in_newCrossref / DOI: 10.1016/j.biopha.2021.111578
  47. 47

    S. Barik, “Systematizing the genomic order and relatedness in the open reading frames (ORFs) of the coronaviruses”, Infection, Genetics and Evolution, 92:104858, Aug 2021. DOI: 10.1016/j.meegid.2021.104858.

    open_in_newCrossref / DOI: 10.1016/j.meegid.2021.104858
  48. 48

    N. W. Chew, Z. G. Ow, V. X. Teo, R. R. Heng, C. H. Ng, C. H. Lee, A. F. Low, M. Y. Chan, T. C. Yeo, H. C. Tan, P. H. Loh, “The Global Impact of the COVID-19 Pandemic on STEMI care: A Systematic Review and Meta-Analysis”, Canadian Journal of Cardiology, 2021 Apr 20. DOI: 10.1016/j.cjca.2021.04.003.

    open_in_newCrossref / DOI: 10.1016/j.cjca.2021.04.003
  49. 49

    T. M. Mitchell, Machine learning, Burr Ridge, IL: McGraw Hill, 45(37):870-7, 1997.

format_quote

How to Cite This Article

D. O., E. D. (2022). Scientometric Analysis of COVID-19 Scholars Publication using Machine Learning. International Journal of Applied Methods in Electronics and Computers, 1-10. https://doi.org/10.18100/ijamec.984201