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Topic evolution, disruption and resilience in early COVID-19 research.

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This study introduces a framework and dashboard to help scientists navigate COVID-19 research. It synthesizes emerging information, identifies research trends, and links new findings to existing knowledge.

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Area of Science:

  • Infectious Diseases
  • Information Science
  • Public Health

Background:

  • The COVID-19 pandemic generated a vast amount of scientific literature.
  • Researchers face challenges in synthesizing this information and connecting it to foundational knowledge.
  • A need exists for tools to efficiently manage and understand the rapidly expanding COVID-19 research landscape.

Purpose of the Study:

  • To develop a framework and dashboard for identifying, retrieving, and understanding COVID-19 knowledge.
  • To assist scientists in navigating the large volume of scholarly articles on COVID-19.
  • To bridge the gap between emerging research and the existing knowledge base.

Main Methods:

  • Utilized Principal Component Decomposition (PCD) for topic analysis.
  • Employed a knowledge mode-based search approach.
  • Applied Hierarchical Topic Tree (HTT) analysis to profile research landscapes and visualize knowledge structures.
  • Incorporated a knowledge model for vaccination research, referencing pre-COVID publications.

Main Results:

  • Analysis of 127,971 COVID-19 papers identified 35 research hotspots with trend analysis.
  • HTT segmented the global knowledge into clinical and public health branches.
  • Identified key future research areas including monoclonal antibody treatments, diabetic vaccination, vaccine effectiveness, and allergic sensitization.

Conclusions:

  • The proposed framework and dashboard effectively synthesize COVID-19 research.
  • The tools aid in understanding research trends and identifying foundational knowledge.
  • Future research directions in vaccination and treatment are highlighted, offering valuable insights for the scientific community.