Evolutionary longitudinal network dynamics of global zoonotic research
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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.
KeywordsCollaboration Evolution Network dynamics Longitudinal study Zoonotic research
Conflict of interest
- Abbasi, A., & Hossain, L. (2011). Investigating attachment behavior of nodes during evolution of a complex social network: A case of a scientific collaboration network. In Knowledge-based and intelligent information and engineering systems (Vol. 6882, pp. 256–264). Lecture notes in computer science.Google Scholar
- Ramos, J. M., Padilla, S., Masia, M., & Gutierrez, F. (2008). A bibliometric analysis of tuberculosis research indexed in PubMed, 1997–2006. International Journal of Tuberculosis and Lung Disease, 12, 1461–1468.Google Scholar
- Romo-Fernández, L. M., Guerrero-Bote, V. P., & Moya-Anegón, F. (2013). Co-word based thematic analysis of renewable energy (1990–2010). Scientometrics, 97(3), 743–765.Google Scholar
- Scott, J. (2000). Social network analysis: A handbook. Beverly Hills: SAGE Publications.Google Scholar
- Takahashi-Omoe, H., & Omoe, K. (2012). Worldwide trends in infectious disease research revealed by a new bibliometric method. In P. K. Roy (Ed.), Insight and control of infectious disease in global scenario. Croatia: Intech Open Science Publishers.Google Scholar
- Uthman, O. A. (2008). HIV/AIDS in Nigeria: A bibliometric analysis. BMC Infectious Diseases, 8(1), 19.Google Scholar
- van Eck, N. J. & Waltman, L. 2013. VOSviewer manual [Online]. http://www.vosviewer.com/download/. Accessed 26 Nov 2013.
- WHO. 1959. Zoonoses. World Health Organization Technical Report Series, Geneva.Google Scholar
- WHO. 2013a. Disease outbreak news (DONs) [Online]. http://www.who.int/csr/don/en/. Accessed 26 Nov 2013.
- WHO. 2013b. Zoonoses and the human–animal–ecosystems interface [Online]. http://www.who.int/zoonoses/en/. Accessed 26 Nov 2013.