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Evolutionary longitudinal network dynamics of global zoonotic research

Author

Listed:
  • Liaquat Hossain

    (University of Hong Kong
    University of Sydney)

  • Faezeh Karimi

    (University of Sydney)

  • Rolf T. Wigand

    (University of Arkansas)

  • John W. Crawford

    (Rothamsted Research)

Abstract

At global and local levels, we are observing an increasing range and rate of disease outbreaks that show evidence of jumping from animals to humans, and from food to humans. Zoonotic infections (i.e. Hendra, swine flu, anthrax) affect animal health and can be deadly to humans. The increasing rate of outbreaks of infectious diseases transferring from animals to humans (i.e. zoonotic diseases) necessitates detailed understanding of the education, research and practice of animal health and its connection to human health. These emerging microbial threats underline the need to exploring the evolutionary dynamics of zoonotic research across public health and animal health. This study investigates the collaboration network of different countries engaged in conducting zoonotic research. We explore the dynamics of this network from 1980 to 2012 based on large scientific data developed from Scopus. In our analyses, we compare several properties of the network including density, clustering coefficient, giant component and centrality measures over time. We also map the network over different time intervals using VOSviewer. We analyzed 5182 publication records. We found United States and United Kingdom as the most collaborative countries working with 110 and 74 other countries in 1048 and 599 cases, respectively. Our results show increasing close collaboration among scientists from the United States, several European countries including United Kingdom, Italy, France, Netherland, Switzerland, China and Australia with scientists from other parts of the world.

Suggested Citation

  • Liaquat Hossain & Faezeh Karimi & Rolf T. Wigand & John W. Crawford, 2015. "Evolutionary longitudinal network dynamics of global zoonotic research," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 337-353, May.
    • Handle: RePEc:spr:scient:v:103:y:2015:i:2:d:10.1007_s11192-015-1557-y
    DOI: 10.1007/s11192-015-1557-y
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    References listed on IDEAS

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    Cited by:

    1. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.

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