Coronavirus: Discover the Structure of Global Knowledge, Hidden Patterns & Emerging Events

Background & Objective:  The present study aimed at exploring the structure of global knowledge, hidden patterns, and emerging Coronavirus events using co-word techniques. Co-word analysis is one of the most efficient scientific methods to analyze the structure and dynamics of knowledge and the general state of research.  Materials & Methods:  This applied research performed using Co-word analysis. The statistical population is 4102 keywords from Web of Science Core Collection indexed documents on Coronavirus retrieved through advanced search (1970-2019). To identify the keywords used to design a search strategy, the Medical Subject Heading browser was utilized. After the keyword editing process, the threshold identified, and UCINET, VOSviewe, and SPSS 16 were used to analyze the data. Results:  The highest frequent keyword was "Severe Acute Respiratory Syndrome (SARS)" with a frequency of 276. Nineteen subject clusters were the result of a hierarchical clustering analysis by the Wardschr('39') method. Clusters 4 and 15 were the biggest ones with nine keywords. Strategic diagram analysis showed that the most prominent Coronavirus clusterschr('39') most prominent clusters are in Quadrant III of the strategic diagram. Conclusion:  The results showed that Coronavirus researchchr('39')s intellectual structure in the form of 19-topic thematic clusters and determining the degree of cluster cohesion makes it possible to discover complex conceptual relationships of valid international Coronavirus research. The results of this paper could also be used to guide medical researchers, especially coronavirus scientists. Medical policymakers can also more effectively present strategic plans by becoming aware of the global knowledge structure, hidden patterns, and emerging international coronavirus events.