Characterizing pandemic-related publications: a retrospective study using spatial citation network analysis

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The COVID-19 pandemic sparked a surge in research across disciplines, offering vital knowledge for addressing the crisis and earning widespread citations. Yet, the spatiotemporal patterns within these citation networks are underexplored. This study uses network analysis to examine pandemic-related publications from 2019 to 2023, building two citation networks: one from internal citations among 7,641 papers and another including their 217,453 external references. The analytical findings reveal a not widespread impact in the citation of pandemic-related publications, suggesting that a small number of studies gained the most of research focus from subsequent studies. Thematically, research shifted from immediate responses (e.g., "lockdown") to broader impacts (e.g., "mental health"), signaling a focus on long-term resilience. Spatially, citations cluster in regions like the eastern U.S., Europe, and East Asia, while areas like Africa and Inner Asia show limited integration, highlighting geographic disparities and imbalanced networks. This analysis sheds light on interdisciplinary and regional collaboration in pandemic research and emphasizes the need for equitable global participation in knowledge networks. These insights offer practical implications for enhancing research dissemination in future health crises.

Original languageEnglish
Article number25
JournalComputational Urban Science
Volume5
Issue number1
DOIs
Publication statusPublished - 8 May 2025

Keywords

  • Academic networks
  • Citation analysis
  • COVID-19
  • Knowledge
  • Urban studies

ASJC Scopus subject areas

  • Urban Studies
  • Artificial Intelligence
  • Computer Science Applications
  • Environmental Science (miscellaneous)

Fingerprint

Dive into the research topics of 'Characterizing pandemic-related publications: a retrospective study using spatial citation network analysis'. Together they form a unique fingerprint.

Cite this