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Identification of Research Priorities during the COVID-19 Pandemic: Implications for Its Management

Author

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  • Jianhong Luo

    (Department of Management Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Minjuan Chai

    (Department of Management Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Xuwei Pan

    (Department of Management Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

Novel coronavirus disease 2019 (COVID-19) pandemic has had a great impact on global production and life in the past period. Countless researchers devoted themselves to rescuing patients and reducing its impact. Analyzing the literature published during the pandemic and identifying the research priorities is of great significance to quickly discover research gaps, rationally allocate scientific research resources, and promote the development of the global research platform. To understand the swing of research priorities during the pandemic, this paper proposed a research priorities identification framework for pandemic based on scientific literature text analysis. Moreover, a research priority metric model was proposed to measure the characteristics of research priorities, and the empirical analysis from COVID-19 scientific literature was conducted to identify the research priorities during the pandemic. As a result, the research priorities identified by the method proposed in this paper discovered the fine-grained dynamic characteristics along with the process in the pandemic outbreak, and based on this, the emergency scientific research response strategies were discussed to give implications for the public health emergency scientific research and management.

Suggested Citation

  • Jianhong Luo & Minjuan Chai & Xuwei Pan, 2021. "Identification of Research Priorities during the COVID-19 Pandemic: Implications for Its Management," IJERPH, MDPI, vol. 18(24), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13105-:d:700605
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    References listed on IDEAS

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    1. José Carlos Ferrão & Mónica Duarte Oliveira & Daniel Gartner & Filipe Janela & Henrique M. G. Martins, 2021. "Leveraging electronic health record data to inform hospital resource management," Health Care Management Science, Springer, vol. 24(4), pages 716-741, December.
    2. Hongyue Zhang & Rajib Shaw, 2020. "Identifying Research Trends and Gaps in the Context of COVID-19," IJERPH, MDPI, vol. 17(10), pages 1-17, May.
    3. Vahe Tshitoyan & John Dagdelen & Leigh Weston & Alexander Dunn & Ziqin Rong & Olga Kononova & Kristin A. Persson & Gerbrand Ceder & Anubhav Jain, 2019. "Unsupervised word embeddings capture latent knowledge from materials science literature," Nature, Nature, vol. 571(7763), pages 95-98, July.
    4. Michael W. Berry & Murray Browne, 2005. "Email Surveillance Using Non-negative Matrix Factorization," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 249-264, October.
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