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Exploring Coronavirus Disease 2019 Risk Factors: A Text Network Analysis Approach
1Department of Nursing, Keimyung College University, Daegu 42601, Republic of Korea.
Journal of Clinical Medicine
|March 27, 2025
Summary
Text network analysis identified key risk factors for severe COVID-19, including age and hypertension. Research focus shifted from acute symptoms to long COVID and vaccine efficacy over time.
Area of Science:
- Public Health
- Epidemiology
- Data Science
Background:
- The COVID-19 pandemic profoundly impacted global health and societies.
- Understanding factors influencing COVID-19 spread and severity is crucial.
Purpose of the Study:
- To analyze interconnections among risk factors for severe COVID-19 using text network analysis.
- To track the evolution of research focus during the pandemic.
Main Methods:
- Text network analysis of published studies (Jan 2020-Dec 2021).
- Identification of key determinants like age, hypertension, and comorbidities.
- Temporal trend analysis of research themes.
Main Results:
- Five clusters of risk factors identified: biomedical, occupational, demographic, behavioral, and complication-related.
- Early research focused on acute COVID-19 clinical characteristics.
- Later research emphasized long COVID, quality of life, and vaccine efficacy against variants (Alpha, Delta, Omicron).
Conclusions:
- Findings provide insights for targeted public health interventions for high-risk groups.
- Text network analysis is a valuable tool for synthesizing complex pandemic data.
- Supports evidence-based decision-making for pandemic preparedness and response.
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