Kang Min-Ah, Lee Soo-Kyoung
Department of Nursing, Keimyung College University, Daegu 42601, Republic of Korea.
Department of Medical Informatics, College of Nursing & Health, Kongju National University, Kongju 32588, Republic of Korea.
J Clin Med. 2025 Mar 19;14(6):2084. doi: 10.3390/jcm14062084.
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. 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. 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. 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.
2019年冠状病毒病（COVID-19）大流行对全球健康、经济和社会产生了重大影响，因此有必要更深入地了解影响其传播和严重程度的因素。本研究采用文本网络分析方法，考察与重症COVID-19相关的各种风险因素之间的关系。通过分析2020年1月至2021年12月发表的研究数据集，本研究确定了关键决定因素，包括年龄、高血压和既往健康状况，同时揭示了它们之间的相互联系。分析揭示了五个主题集群：生物医学、职业、人口统计学、行为和并发症相关因素。时间趋势分析揭示了研究重点随时间的明显变化。2020年初，研究主要关注COVID-19的即时临床特征和急性并发症。到2021年年中，研究越来越强调长期COVID，突出其长期症状和对生活质量的影响。与此同时，疫苗效力成为一个主要话题，研究评估针对Alpha、Delta和Omicron等新出现病毒变种的保护率。这种不断演变的情况凸显了COVID-19研究的动态性质以及相应公共卫生策略的调整。这些发现为有针对性的公共卫生干预提供了宝贵见解，强调需要制定量身定制的策略，以减轻高危人群的严重后果。本研究证明了文本网络分析作为一种强大工具的潜力，可用于综合复杂数据集，并为大流行防范和应对中的循证决策提供信息。
