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

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.