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The new science of COVID-19: A Bibliographic and Network Analysis

Xuezhou Fan

TL;DR

This study systematically analyzes COVID-19–related publications from 2020 using bibliometric indicators and network analysis to map research trends, collaboration patterns, and influential actors. It leverages Web of Science data and a comprehensive preprocessing pipeline to produce descriptive statistics, time-series insights, and multi-layer networks (co-authorship, country/institution collaborations, research areas, and keywords). Key findings include a rapid rise in publications, dominant contributions from the USA, China, and the UK, and a co-authorship network exhibiting small-world and scale-free properties, with mental health topics notably prevalent. The work provides a detailed, data-driven snapshot of the 2020 COVID-19 research landscape and highlights the ongoing need for globally coordinated studies.

Abstract

Since the outbreak of the COVID-19, there have been many scientific publications studying the COVID-19. The purpose of this study is to identify the research trend, collaboration pattern, most influential elements, etc. from scientific publications related to COVID-19 in 2020, by using bibliographic analysis and network analysis. In Chapter 1 and Chapter 2, motivation behind this paper is introduced. Some previous similar studies are discussed. Comparisons are made in different aspects, such as data collection methods, data analysis tools and methods, etc. Their advantages and limitations compared to this paper are also addressed. In Chapter 3, important concepts used in this paper related to bibliographic analysis such as h-index and g-index, and network analysis such as centrality measures and assortativity are introduced. Networks with small-world property and scale-free property will also be studied. In Chapter 4 and Chapter 5, the way the data are obtained for the analysis of this paper is introduced step by step. Full result is shown. In Chapter 6, conclusions are arrived. A general growing trend of the number of the publications is observed, due to the efforts made by scientific researchers. Meanwhile, measures should also be taken to encourage future study in this field.

The new science of COVID-19: A Bibliographic and Network Analysis

TL;DR

This study systematically analyzes COVID-19–related publications from 2020 using bibliometric indicators and network analysis to map research trends, collaboration patterns, and influential actors. It leverages Web of Science data and a comprehensive preprocessing pipeline to produce descriptive statistics, time-series insights, and multi-layer networks (co-authorship, country/institution collaborations, research areas, and keywords). Key findings include a rapid rise in publications, dominant contributions from the USA, China, and the UK, and a co-authorship network exhibiting small-world and scale-free properties, with mental health topics notably prevalent. The work provides a detailed, data-driven snapshot of the 2020 COVID-19 research landscape and highlights the ongoing need for globally coordinated studies.

Abstract

Since the outbreak of the COVID-19, there have been many scientific publications studying the COVID-19. The purpose of this study is to identify the research trend, collaboration pattern, most influential elements, etc. from scientific publications related to COVID-19 in 2020, by using bibliographic analysis and network analysis. In Chapter 1 and Chapter 2, motivation behind this paper is introduced. Some previous similar studies are discussed. Comparisons are made in different aspects, such as data collection methods, data analysis tools and methods, etc. Their advantages and limitations compared to this paper are also addressed. In Chapter 3, important concepts used in this paper related to bibliographic analysis such as h-index and g-index, and network analysis such as centrality measures and assortativity are introduced. Networks with small-world property and scale-free property will also be studied. In Chapter 4 and Chapter 5, the way the data are obtained for the analysis of this paper is introduced step by step. Full result is shown. In Chapter 6, conclusions are arrived. A general growing trend of the number of the publications is observed, due to the efforts made by scientific researchers. Meanwhile, measures should also be taken to encourage future study in this field.
Paper Structure (63 sections, 13 equations, 24 figures, 11 tables)

This paper contains 63 sections, 13 equations, 24 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Method
  4. Bibliographic analysis
  5. h-index
  6. g-index
  7. Pearson's correlation coefficient (for a population)
  8. Network analysis
  9. Co-authorship network
  10. Country collaboration network
  11. Institution collaboration network
  12. Research area co-occurrence network
  13. Keyword co-occurrence network
  14. Centrality
  15. Degree centrality
  16. ...and 48 more sections

Figures (24)

  • Figure 1: Data filtering
  • Figure 2: Document type
  • Figure 3: Language
  • Figure 4: Publication source
  • Figure 5: Page count
  • ...and 19 more figures