Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Abstract

Background and objective: The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis.

Methods: We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators.

Results: The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images.

Conclusions: The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.

Keywords: Bibliometric analysis; COVID-19 diagnosis tool; COVID-19 pandemic; Convolutional neural network; Machine learning.

Figure 1. Graphical representation of the survey structure.

Figure 2. Article selection process.

Figure 3. Taxonomy of the machine learning algorithms adopted in fighting COVID-19.

Figure 4. Visual representation of COVID-19 data extracted from different projects.

Figure 5. Machine learning algorithms adopted in fighting COVID-19.

Figure 6. Different aspect of machine learning applications in fighting COVID-19 pandemic.

Figure 7. Trend of publications on machine learning applications in fighting COVID-19.

Figure 8. Bibliographic coupling among the authors.

Two clusters, namely red (left) and green (right), correspond to all authors working on similar research fields “COVID-19” and citing the same sources in their reference listings.

Figure 9. Bibliographic coupling among the countries.

Red represents China and the USA with the highest strength in terms of contributions, after which comes India and Iran as the next countries within the red node. Green represents Hong Kong, which appears to have the highest strength, whereas blue is for the United Kingdom and Saudi Arabia that have the highest strength. Yellow denotes Japan, Singapore, Thailand, and Taiwan as the highest contributors. Purple refers to Italy and Canada as the two contributing countries.

Figure 10. Bibliographic coupling among institutions.

Two clusters, namely red (left) and green (right), correspond to all authors working on similar research fields “COVID-19” and citing the same sources in their reference listings.

Figure 11. Bibliographic coupling among the journals.

Three clusters are depicted on the map with red (left), blue (bottom-centre), and green (right) colors. Each cluster shows COVID-19 published papers with more common reference lists among the associated journals.

Figure 12. Co-authorship and authors’ analysis.

The four main clusters, namely, blue (right), red (bottom-centre), green (top-centre), pink (left) match all the major co-authors and authors publishing together or working on similar research fields.

Figure 13. Author citation by institution.

Six clusters are represented using different colors to denotes authors citation counts per institution, for which only the red (bottom-left) cluster has the highest number of author citations from two institutions in China.

Figure 14. Author citation by journal source.

Three major clusters, namely, red (left), lemon-green (top-right), and green (bottom-left) were identified in the analysis, as the top-cited journal sources as per author publications.

Figure 15. Visual representation of the challenges.