The multidisciplinary nature of COVID-19 research

Main Article Content

Ricardo Arencibia-Jorge
Lourdes García-García
Ernesto Galban-Rodriguez
Humberto Carrillo-Calvet


Objective. We analyzed the scientific output after COVID-19 and contrasted it with studies published in the aftermath of seven epidemics/pandemics: Severe Acute Respiratory Syndrome (SARS), Influenza A virus H5N1 and Influenza A virus H1N1 human infections, Middle East Respiratory Syndrome (MERS), Ebola virus disease, Zika virus disease, and Dengue.

Design/Methodology/Approach. We examined bibliometric measures for COVID-19 and the rest of the studied epidemics/pandemics. Data were extracted from Web of Science, using its journal classification scheme as a proxy to quantify the multidisciplinary coverage of scientific output. We proposed a novel Thematic Dispersion Index (TDI) for the analysis of pandemic early stages. 

Results/Discussion. The literature on the seven epidemics/pandemics before COVID-19 has shown explosive growth of the scientific production and continuous impact during the first three years following each emergence or re-emergence of the specific infectious disease. A subsequent decline was observed with the progressive control of each health emergency. We observed an unprecedented growth in COVID-19 scientific production. TDI measured for COVID-19 (29,4) in just six months, was higher than TDI of the rest (7,5 to 21) during the first three years after epidemic initiation.

Conclusions. COVID-19 literature showed the broadest subject coverage, which is clearly a consequence of its social, economic, and political impact. The proposed indicator (TDI), allowed the study of multidisciplinarity, differentiating the thematic complexity of COVID-19 from the previous seven epidemics/pandemics.

Originality/Value. The multidisciplinary nature and thematic complexity of COVID-19 research were successfully analyzed through a scientometric perspective.


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Arencibia-Jorge, R., García-García, L., Galban-Rodriguez, E., & Carrillo-Calvet, H. (2020). The multidisciplinary nature of COVID-19 research. Iberoamerican Journal of Science Measurement and Communication, 1(1).
Original articles


Arencibia-Jorge, R., Vega-Almeida, R. L., & Carrillo-Calvet, H. (2020). Evolución y alcance multidisciplinar de tres técnicas de análisis bibliométrico. Palabra Clave (La Plata), 10(1), e102.

Belli, S., Mugnaini, R., Baltà, J., & Abadal, E. (2020). Coronavirus mapping in scientific publications: When science advances rapidly and collectively, is access to this knowledge open to society? Scientometrics 124 (3), 2661-2685.

Chahrour, M., Assi, S., Bejjani, M., Nasrallah, A. A., Salhab, H., Fares, M., et al. (2020). A bibliometric analysis of Covid-19 research activity: A call for increased output. Cureus, 12(3), e7357.

Chen, C. (2017) Science mapping: a systematic review of the literature. Journal of Data and Information Science, 2(2), 1-40.

Darsono, D., Rohmana, J. A., & Busro, B. (2020). Against COVID-19 Pandemic: Bibliometric Assessment of World Scholars' International Publications related to COVID-19. Jurnal Komunikasi Ikatan Sarjana Komunikasi Indonesia, 5(1), 75-89.

De Felice, F., & Polimeni, A. (2020). Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis. In vivo, 34(3 suppl), 1613-1617.

Dehghanbanadaki, H., Seif, F., Vahidi, Y., Razi, F., Hashemi, E., Khoshmirsafa, M., et al. (2020). Bibliometric analysis of global scientific research on Coronavirus (COVID-19). Medical Journal of The Islamic Republic of Iran (MJIRI), 34(1), 354-362.

El Mohadab, M., Bouikhalene, B., & Safi, S. (2020). Bibliometric method for mapping the state of the art of scientific production in Covid-19. Chaos, Solitons & Fractals, 139, 110052.

Fan, V., Jamison, D. T., & Summers, L. H. (2018). Pandemic risk: how large are the expected losses? Bulletin of the World Health Organization, 96(2), 129-134.

Garfield, E., 2006. The history and meaning of the journal impact factor. Jama 295 (1), 90-93.

Hamidah, I., Sriyono, S., & Hudha, M. N. (2020). A Bibliometric Analysis of Covid-19 Research using VOSviewer. Indonesian Journal of Science and Technology, 5(2), 34-41.

Herrera-Viedma, E., López-Robles, J. R., Guallar, J., & Cobo, M. J. (2020). Global trends in coronavirus research at the time of Covid-19: A general bibliometric approach and content analysis using SciMAT. El Profesional de la Información, 29(3), e290322.

Huber, C., Finelli, L., & Stevens, W. (2018). The economic and social burden of the 2014 Ebola outbreak in West Africa. The Journal of Infectious Diseases, 218(Suppl. 5), S698-S704.

Johanson, M. A., Reich, N. G., Meyers, L. A., & Lipsitch, M. (2018). Pre-prints: An underutilized mechanism to accelerate outbreak science. PLoS Medicine, 15(4), e1002549.

Kambhampati, S. B., Vaishya, R., & Vaish, A. (2020). Unprecedented surge in publications related to COVID-19 in the first three months of pandemic: A bibliometric analytic report. Journal of Clinical Orthopaedics and Trauma, 11(Suppl 3), S304.

Keogh-Brown, M. R., & Smith, R. D. (2008). The economic impact of SARS: how does the reality match the predictions? Health policy, 88(1), 110-120.

Klavans, R., & Boyack, K. W. (2011). Using global mapping to create more accurate document-level maps of research fields. Journal of the American Society for Information Science and Technology, 62(1), 1-18.

Kozlakidis, Z., Abduljawad, J., Al Khathaami, A. M., Schaper, L., & Stelling, J. (2020). Global health and data-driven policies for emergency responses to infectious disease outbreaks. The Lancet Global Health, August 10.

Kuhar, M., & Fatović-Ferenčić, S. (2020). Victories and defeats: battles with pandemics caused by viruses during the last one hundred years. Liječnički vjesnik, 142(3-4), 107-113.

Leydesdorff, L., & Bornmann, L. (2016). The operationalization of fields as WoS subject categories (WCs) in evaluative bibliometrics: The cases of library and information science and science & technology studies. Journal of the Association for Information Science and Technology, 67(3), 707-714.

Lou, J., Tian, S. J., Niu, S. M., Kang, X. Q., Lian, H. X., Zhang, L. X., et al. (2020). Coronavirus disease 2019: a bibliometric analysis and review. Eur Rev Med Pharmacol Sci, 24(6), 3411-21.

Moradian, N., Ochs, H. D., Sedikies, C., Hamblin, M. R., Camargo, C. A., Martinez, J. A., et al. (2020). The urgent need for integrated science to fight COVID-19 pandemic and beyond. Journal of Translational Medicine, 18(1), 205.

Moschini, U., Fenialdi, E., Daraio, C., Ruocco, G., & Molinari, E. (2020). A comparison of three multidisciplinarity indices based on the diversity of Scopus subject areas of 'authors' documents, their bibliography and their citing papers. Scientometrics, May 15.

Myers, K. R., Tham, W. Y., Yin, Y., Cohodes, N., Thursby, J. G., Thursby, M.C., et al. (2020). Unequal effects of the COVID-19 pandemic on scientists. Nature human behaviour, 4(9), 880-3.

Peters, M. A., Jandrić, P., & McLaren, P. (2020). Viral modernity? Epidemics, infodemics, and the '' 'bioinformational' paradigm. Educational Philosophy and Theory, 1-23.

Pike, J., Bogich, T., Elwood, S., Finnoff, D. C., & Daszak, P. (2014). Economic optimization of a global strategy to address the pandemic threat. Proceedings of the National Academy of Sciences, 111(52), 18519-18523.

Porta Serra, M. (2014). A Dictionary of epidemiology. Oxford: Oxford University Press.

Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719-745.

Smart, P., 2020. Publishing during pandemic: Innovation, collaboration, and change. Learned Publishing, 33(3), 194-197.

Tao, Z., Zhou, S., Yao, R., Wen, K., Da, W., Meng, Y., et al. (2020). COVID-19 will stimulate a new coronavirus research breakthrough: a 20-year bibliometric analysis. Annals of Translational Medicine, 8(8), 528.

Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of informetrics, 5(1), 14-26.

Wang, T., Du, Z., Zhu, F., Cao, Z., An, Y., Gao, Y., et al. (2020). Comorbidities and multi-organ injuries in the treatment of COVID-19. The Lancet, 395(10228), e52.

Wang, X., Wang, Z., Huang, Y., Chen, Y., Zhang, Y., Ren, H., et al. (2017). Measuring interdisciplinarity of a research system: detecting distinction between publication categories and citation categories. Scientometrics, 111, 2023–2039.

Zhai, F., Zhai, Y., Cong, C., Song, T., Xiang, R., Feng, T., et al. (2020). Research Progress of Coronavirus Based on Bibliometric Analysis. International Journal of Environmental Research and Public Health, 17(11), 3766.

Zhang, L., Zhao, W., Sun, B., Huang, Y., & Glänzel, W. (2020). How scientific research reacts to international public health emergencies: a global analysis of response patterns. Scientometrics, 124, 747-773. 10.1007/s11192-020-03531-4

Zhou, Y., & Chen, L. (2020). Twenty-Year Span of Global Coronavirus Research Trends: A Bibliometric Analysis. International Journal of Environmental Research and Public Health, 17(9), 3082.