en Medical Biology Medical Biology مقاله پژوهشی Original Research Article <div style="text-align: center;"><div style="clear:both;"></div></div> <div dir="ltr" style="text-align: justify;"><span style="font-family:Times New Roman;"><span style="font-size:14px;"><strong><span style="background:#2D7F8F;"><span style="color:white;">Background & Objective:</span></span></strong> &nbsp;<span style="color:black;">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.</span><br> <strong><span style="letter-spacing:-.1pt;background:#2D7F8F;"><span style="color:white;">&nbsp;Materials & Methods:</span></span></strong> &nbsp;<span style="letter-spacing:-.1pt;"><span style="color:black;">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.</span></span><br> <strong><span style="background:#2D7F8F;"><span style="color:white;">Results: </span></span></strong>&nbsp;<span style="letter-spacing:-.1pt;"><span style="color:black;">The highest frequent keyword was &quot;Severe Acute Respiratory Syndrome (SARS)&quot; with a frequency of 276. Nineteen subject clusters were the result of a hierarchical clustering analysis by the Wards&#39; method. Clusters 4 and 15 were the biggest ones with nine keywords. Strategic diagram analysis showed that the most prominent Coronavirus clusters&#39; most prominent clusters are in </span></span><span style="letter-spacing:-.1pt;"><span style="color:black;">Quadrant III</span></span><span style="letter-spacing:-.1pt;"><span style="color:black;"> of the strategic diagram<span dir="RTL">.</span> </span></span><br> <strong><span style="background:#2D7F8F;"><span style="color:white;">Conclusion: </span></span></strong>&nbsp;<span style="color:black;">The results showed that Coronavirus research&#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.</span></span></span></div> <div style="clear:both;"></div> Coronavirus, COVID-19, 2019-nCoV, Coronavirus Disease 2019, Scientometrics, Co-word Analysis, strategic diagram. 253 264 http://journal.zums.ac.ir/browse.php?a_code=A-10-5588-1&slc_lang=en&sid=1 Farshid Danesh farshiddanesh@ricest.ac.ir 5200319475328460054240 5200319475328460054240 Yes Information Management Research Department, Regional Information Center for Science and Technology (RICeST), Shiraz, Iran Somayeh GhaviDel vahab.p@gmail.com 5200319475328460054241 5200319475328460054241 No Department of Knowledge and Information Science, Kharazmi University, Tehran, Iran Vahhab Piranfar vahab.p@gmail.com 5200319475328460054242 5200319475328460054242 No Research and Development Department, Farname Inc, Thornhill, Canada