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Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature

Author

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  • Milad Haghani

    (The University of Sydney)

  • Michiel C. J. Bliemer

    (The University of Sydney)

Abstract

During the current century, each major coronavirus outbreak has triggered a quick and immediate surge of academic publications on its respective topic. The spike in research publications following the 2019 Novel Coronavirus (Covid-19) outbreak, however, has been like no other. The global crisis caused by the Covid-19 pandemic has mobilised scientific efforts at an unprecedented scale. In less than 5 months, more than 12,000 research items and in less than seven months, more than 30,000 items were indexed, while it is projected that the number could exceed 80,000 by the end of 2020, should the current trend continues. With the health crisis affecting all aspects of life, research on Covid-19 seems to have become a focal point of interest across many academic disciplines. Here, scientometric aspects of the Covid-19 literature are analysed and contrasted with those of the two previous major coronavirus diseases, i.e., Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). The focus is on the co-occurrence of key-terms, bibliographic coupling and citation relations of journals and collaborations between countries. Interesting recurring patterns across all three literatures were discovered. All three outbreaks have commonly generated three distinct cohorts of studies: (i) studies linked to public health response and epidemic control, (ii) studies on chemical constitution of the virus; and (iii) studies related to treatment, vaccine and clinical care. While studies affiliated with category (i) seem to have been relatively earliest to emerge, they have overall received relatively smaller number of citations compared to publications the two other categories. Covid-19 studies seem to have been disseminated across a broader variety of journals and across a more diverse range of subject areas. Clear links are observed between the geographical origins of each outbreak as well as the local geographical severity of each outbreak and the magnitude of research originated from regions. Covid-19 studies also display the involvement of authors from a broader variety of countries compared to SARS and MERS. Considering the speed at which the Covid-19-related literature is accumulating, an interesting dimension that warrants further exploration could be to assess if the quality and rigour of these publications have been affected.

Suggested Citation

  • Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
  • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03706-z
    DOI: 10.1007/s11192-020-03706-z
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    References listed on IDEAS

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    1. Editorial, 2020. "Covid-19 and Climate Change," Journal, Review of Agrarian Studies, vol. 10(1), pages 5-6, January-J.
    2. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Torres, Delfim F.M., 2020. "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Deqiao Tian & Tao Zheng, 2015. "Emerging infectious disease: trends in the literature on SARS and H7N9 influenza," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 485-495, October.
    4. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    5. Barmparis, G.D. & Tsironis, G.P., 2020. "Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    6. Bekiros, Stelios & Kouloumpou, Dimitra, 2020. "SBDiEM: A new mathematical model of infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    7. Ronald N. Kostoff & Stephen A. Morse, 2011. "Structure and infrastructure of infectious agent research literature: SARS," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 195-209, January.
    8. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    9. Nicholas V. Olijnyk, 2015. "An algorithmic historiography of the Ebola research specialty: mapping the science behind Ebola," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 623-643, October.
    10. Postnikov, Eugene B., 2020. "Estimation of COVID-19 dynamics “on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions?," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    11. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
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    1. > Economics of Welfare > Health Economics > Economics of Pandemics

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    Cited by:

    1. Lalinsky, Tibor & Pál, Rozália, 2022. "Distribution of COVID-19 government support and its consequences for firm liquidity and solvency," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 305-335.
    2. Milad Haghani & Pegah Varamini, 2021. "Temporal evolution, most influential studies and sleeping beauties of the coronavirus literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7005-7050, August.
    3. Eva Seidlmayer & Tetyana Melnychuk & Lukas Galke & Lisa Kühnel & Klaus Tochtermann & Carsten Schultz & Konrad U. Förstner, 2024. "Research topic displacement and the lack of interdisciplinarity: lessons from the scientific response to COVID-19," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5141-5179, September.
    4. Raghu Raman & Ricardo Vinuesa & Prema Nedungadi, 2021. "Bibliometric Analysis of SARS, MERS, and COVID-19 Studies from India and Connection to Sustainable Development Goals," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    5. Jiban K. Pal, 2021. "Visualizing the knowledge outburst in global research on COVID-19," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4173-4193, May.
    6. Mona Farouk Ali, 2022. "Between panic and motivation: did the first wave of COVID-19 affect scientific publishing in Mediterranean countries?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3083-3115, June.
    7. Rousseau, Ronald & Garcia-Zorita, Carlos & Sanz-Casado, Elías, 2023. "Publications during COVID-19 times: An unexpected overall increase," Journal of Informetrics, Elsevier, vol. 17(4).
    8. Aliakbar Pourhatami & Mohammad Kaviyani-Charati & Bahareh Kargar & Hamed Baziyad & Maryam Kargar & Carlos Olmeda-Gómez, 2021. "Mapping the intellectual structure of the coronavirus field (2000–2020): a co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6625-6657, August.
    9. Lalinsky, Tibor & Pál, Rozália, 2021. "Efficiency and effectiveness of the COVID-19 government support: Evidence from firm-level data," EIB Working Papers 2021/06, European Investment Bank (EIB).
    10. Gabriela F. Nane & Nicolas Robinson-Garcia & François Schalkwyk & Daniel Torres-Salinas, 2023. "COVID-19 and the scientific publishing system: growth, open access and scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 345-362, January.
    11. Török, Ádám & Konka, Boglárka & Nagy, Andrea Magda, 2023. "A koronavírus-járvány a közgazdasági szakirodalomban. Egy új határterület tudománymetriai elemzése [The coronavirus pandemic in the economics literature. The scientometric analysis of a new discipl," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 284-304.
    12. Xiaoyan Liang & Xiwei Zhang & Renee Paulet & Leven Jianwen Zheng, 2022. "A Literature Review of the COVID-19 Pandemic’s Effect on Sustainable HRM," Sustainability, MDPI, vol. 14(5), pages 1-26, February.
    13. Mario Coccia, 2021. "Evolution and structure of research fields driven by crises and environmental threats: the COVID-19 research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9405-9429, December.
    14. Breno Santana Santos & Ivanovitch Silva & Luciana Lima & Patricia Takako Endo & Gisliany Alves & Marcel da Câmara Ribeiro-Dantas, 2022. "Discovering temporal scientometric knowledge in COVID-19 scholarly production," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1609-1642, March.
    15. Constantin Bürgi & Klaus Wohlrabe, 2022. "The influence of Covid-19 on publications in economics: bibliometric evidence from five working paper series," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5175-5189, September.
    16. Yves Fassin, 2021. "Research on Covid-19: a disruptive phenomenon for bibliometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5305-5319, June.
    17. Yujie Zhang & Hongzhen Li & Jingyi Mao & Guoxiu He & Yunhan Yang & Zhuoren Jiang & Yufeng Duan, 2023. "COVID-19: a disruptive impact on the knowledge support of references," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4791-4823, August.
    18. Ming-Sin Choong & Ying-Che Hsieh & Chan-Yuan Wong, 2024. "Resilient or Resistant: Pandemic Crisis and Early Observations of Different Preventive Capabilities from Cumulativeness of Scientific Research Points of View," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 11976-12005, September.

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