Journal of Clinical Medicine, Год журнала: 2025, Номер 14(6), С. 2084 - 2084
Опубликована: Март 19, 2025
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 severity. Methods: This study employed text network analysis to examine relationships among various risk associated with severe COVID-19. Analyzing dataset published studies from January 2020 December 2021, this identifies key determinants, including age, hypertension, pre-existing health conditions, while uncovering their interconnections. Results: reveals five thematic clusters: biomedical, occupational, demographic, behavioral, complication-related factors. Temporal trend distinct shifts in research focus over time. In early 2020, primarily addressed immediate clinical characteristics acute complications By mid-2021, increasingly emphasized long COVID, highlighting prolonged symptoms impact on quality life. Concurrently, vaccine efficacy became dominant topic, assessing protection rates against emerging viral variants, such as Alpha, Delta, Omicron. evolving landscape underscores dynamic nature COVID-19 adaptation public strategies accordingly. Conclusions: These findings offer valuable insights for targeted interventions, emphasizing need tailored mitigate outcomes high-risk groups. demonstrates potential robust tool synthesizing complex datasets informing evidence-based decision-making preparedness response.
Язык: Английский