Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/115164
Title: Characterizing pandemic-related publications : a retrospective study using spatial citation network analysis
Authors: Xiao, Z 
Fan, L 
Yu, Z 
Liu, X 
Issue Date: Dec-2025
Source: Computational urban science, Dec. 2025, v. 5, no. 1, 25
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.
Keywords: Academic networks
Citation analysis
COVID-19
Knowledge
Urban studies
Publisher: Springer Cham
Journal: Computational urban science 
EISSN: 2730-6852
DOI: 10.1007/s43762-025-00184-y
Rights: © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The following publication Xiao, Z., Fan, L., Yu, Z. et al. Characterizing pandemic-related publications: a retrospective study using spatial citation network analysis. Comput.Urban Sci. 5, 25 (2025) is available at https://doi.org/10.1007/s43762-025-00184-y.
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