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

<b>Background/Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.