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. 2025 Mar 19;14(6):2084.
doi: 10.3390/jcm14062084.

Exploring Coronavirus Disease 2019 Risk Factors: A Text Network Analysis Approach

Min-Ah Kang  1 , Soo-Kyoung Lee  2
Affiliations

Exploring Coronavirus Disease 2019 Risk Factors: A Text Network Analysis Approach

Min-Ah Kang et al. J Clin Med. .

Abstract

Background/Objectives: The coronavirus disease 2019 (COVID-19) pandemic has significantly affected global health, economies, and societies, necessitating a deeper understanding of the factors influencing its spread and severity. Methods: This study employed text network analysis to examine relationships among various risk factors associated with severe COVID-19. Analyzing a dataset of published studies from January 2020 to December 2021, this study identifies key determinants, including age, hypertension, and pre-existing health conditions, while uncovering their interconnections. Results: The analysis reveals five thematic clusters: biomedical, occupational, demographic, behavioral, and complication-related factors. Temporal trend analysis reveals distinct shifts in research focus over time. In early 2020, studies primarily addressed immediate clinical characteristics and acute complications of COVID-19. By mid-2021, research increasingly emphasized long COVID, highlighting its prolonged symptoms and impact on quality of life. Concurrently, vaccine efficacy became a dominant topic, with studies assessing protection rates against emerging viral variants, such as Alpha, Delta, and Omicron. This evolving landscape underscores the dynamic nature of COVID-19 research and the adaptation of public health strategies accordingly. Conclusions: These findings offer valuable insights for targeted public health interventions, emphasizing the need for tailored strategies to mitigate severe outcomes in high-risk groups. This study demonstrates the potential of text network analysis as a robust tool for synthesizing complex datasets and informing evidence-based decision-making in pandemic preparedness and response.

Keywords: coronavirus disease 2019; risk factors; text network analysis.

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Conflict of interest statement

The authors declare no conflicts of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure A1
Figure A1
Spring network map of severe COVID-19 risk factors.
Figure A2
Figure A2
Clustering of severe COVID-19 risk factors: Groups 1–4.
Figure A3
Figure A3
Group 5: Complication factors.
Figure 1
Figure 1
PRISMA flow diagram of article selection process.
Figure 2
Figure 2
Monthly publications of severe COVID-19 risk factor studies.
Figure 3
Figure 3
Journal distribution of search articles.
Figure 4
Figure 4
Phase setting by Jaccard similarity.
See this image and copyright information in PMC

References

    1. World Health Organization Coronavirus Disease (COVID-19) Pandemic. 2020. [(accessed on 12 February 2022)]. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
    1. Centers for Disease Control and Prevention COVID-19 Overview and Infection Prevention and Control Priorities in Non-U.S. Healthcare Settings. [(accessed on 12 February 2022)]; Available online: https://archive.cdc.gov/www_cdc_gov/coronavirus/2019-ncov/hcp/non-us-set....
    1. Dong E., Du H., Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 2020;20:533–534. doi: 10.1016/S1473-3099(20)30120-1. - DOI - PMC - PubMed
    1. Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., Xiang J., Wang Y., Song B., Gu X., et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet. 2020;395:1054–1062. doi: 10.1016/S0140-6736(20)30566-3. - DOI - PMC - PubMed
    1. Wang C., Horby P.W., Hayden F.G., Gao G.F. A novel coronavirus outbreak of global health concern. Lancet. 2020;395:470–473. doi: 10.1016/S0140-6736(20)30185-9. - DOI - PMC - PubMed

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