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Exploring the Knowledge Structure and Trends for Severe COVID-19 Risk Factors Using Text Network Analysis.
1Department of Nursing, Keimyung College University, Daegu, South Korea.
Studies in Health Technology and Informatics
|January 25, 2024
Summary
This study mapped severe COVID-19 risk factors using text network analysis of over 22,000 papers. Key themes include biomedical, occupational, demographic, behavioral factors, and complications, revealing chronological trends in severe COVID-19 risk research.
Area of Science:
- Public Health and Epidemiology
- Data Science and Network Analysis
- Infectious Diseases
Background:
- Severe COVID-19 risk factors are complex and multifactorial.
- Understanding the evolving knowledge landscape is crucial for effective public health strategies.
- Previous research has not systematically analyzed the structure and trends of severe COVID-19 risk factors.
Purpose of the Study:
- To identify the knowledge structure and emerging trends in severe COVID-19 risk factors.
- To analyze the relationships between different categories of risk factors.
- To provide a systematic overview of research on severe COVID-19 risk.
Main Methods:
- Text network analysis of 22,628 research papers published between January 2020 and December 2021.
- Utilized Text Rank analyzer and Gephi software for analysis and visualization.
- Grouped identified risk factors into five central themes: biomedical, occupational/environmental, demographic, health behavior, and complications.
Main Results:
- Identified five core themes encompassing biomedical, occupational/environmental, demographic, health behavior factors, and complications.
- Revealed chronological trends in the emergence and focus of different risk factors over time.
- Established a knowledge structure illustrating the interconnectedness of various severe COVID-19 risk factors.
Conclusions:
- The study provides a systematic understanding of the complex landscape of severe COVID-19 risk factors.
- The identified themes and trends can inform future research and public health interventions.
- Text network analysis is a valuable tool for mapping knowledge structures in rapidly evolving research fields like COVID-19.
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