COVID-19 epidemic is an emergent infectious disease caused by respiratory syndrome coronavirus 2 and has been named SARS-CoV-2 (formerly 2019-nCoV) and COVID-19 disease (1) (2) (3) (4) (5) (6) (7) (8) . Coronaviruses were first described in 1965 (1) by Tyrell and Bynoe, who cultured the viruses from patients with what was thought common colds symptoms (2, 3) . The term "Coronavirus," which described the characteristic microscopic morphology of these viruses, was officially coined in 1968 (3) . SARS CoV-2 belongs to the B-lineage of beta-coronaviruses and is closely related to the SARS-CoV virus. Currently, the suspicion of infection with 2019-nCoV requires two elements as indicators of the case: presence of fever and symptoms of respiratory disease. SARS-CoV-2 apparently managed to make its transition from animals to humans at the seafood market in Wuhan, China (4,5). Wuhan, is the same place where one of the largest biosafety level 4 (BSL-4) Asia´s laboratories is located, where European, Canadian and American experts collaborate. The initial clinical sign of COVID-19-related disease that allowed detection was pneumonia.
Observations so far suggest an incubation average period of five days.
The literature on the medical, virology and epidemiology aspects of the COVID-19 emergency are beginning to abound, revealing important pieces of data towards the understanding and possible solutions of this global problem (4-8). However, very scarce studies are yet available on the analysis of the conceptual mapping of coronaviruses, in terms of the relationships among all the data produced in the last 25 years in this area. Accordingly, this present investigation shows an analysis of the published documents of COVID-19 in reliable sources, including Medline, Web of Science and Scopus, and the results of the mentioned bases were integrated into a comprehensive bibliometric analysis, including not only basic and applied research, but also how this knowledge begins to involve intellectual property (11).
The software package Mendeley, a recognized references manager, was employed to collect 547 items of different databases such as Medline, Web of Science and Scopus. To map the knowledge translation of COVID-19, we used the statistical indicator R and graphics, networks and histograms through the software package "Bibliometrix". This methodology has proven very insightful to the knowledge structure of complex diseases cases such as cancer (9) and ebola (10). Figure 1 shows schematically the analytical methodology we have employed (9-10).
Data was extracted from different scientific and medical databases to analyze the different and relevant concepts that revolve around COVID-19, including 2019-nCoV and SARS-CoV-2. Then, a data framework from different articles was created, along with a structure of the involved knowledge, that allows to understand the relationships among the different variables. Thus, results from the different Medline, Web of Science and Scopus databases can be appreciated unified and not separated from each other, as there is the case of the few studies available, which present relevant data, although disconnected and somehow incoherent. Therefore, to estimate the relative risk of transmission of COVID-19, we considered the most relevant scientific databases to estimate the risk of using a single analysis.
We analyzed 547 articles from the information contained in different scientific databases, based on the key terms, so the statistical applications determine the behavior of the variables, as summarized in Table 1 . Table 1 .
To create a conceptual structure of COVID-19 one requires not only to properly identify the relevant concepts involved, but also their time and geographical evolution. This allows to identify that 2020 has been the year with the highest number of scientific articles, as shown in Table 2 . This would be an obvious result, given the current worldwide concern on this disease. However, the time evolution of the knowledge related to associated keywords reveals interesting facts.
Year Articles  2003  2  2004  1  2006  1  2011  1  2019  1  2020  522   Table 2 . Yearly scientific production
The origin of the references is very important, to identify the interest and influence of various geographical locations, as well as the disciplines which were first attracted to this phenomenon, as observed in Table 1 , which provides a first clue that, despite
representing an area of scientific interest and public health danges, coronavirus has been basically a set of isolated efforts, localized in few authors and institutions who focused on this subject, with a relatively low Collaboration Index. Certainly, there are are few single-autored papers, but the great percentage correspond to teams working consistently in the area for nearly 20 years. The most relevant and most cited sources are summarized in Table 3 . Words  Occurrences  patients  632  outbreak  80  novel  196  ncov  601  infection  248  humans  16  epidemiology  8  disease outbreaks  8  disease  72  covid  613  coronavirus infections  13  coronavirus  677  coronavirus  8  china  435  cases  445  TOTAL  4,052   Table 4 . Ocurrence of keywords Figure 7 shows the concurrence network of the set of articles analyzed.
When Centrality is analyzed, Table 5 , the term "Humans" is the most important one, followed by "China" nd "Coronavirus infections", with the rest of the nodes having very little contribution. This allows to quantify the influence of those particular nodes within the network of knowledge formed by all the articles and patents. Table 5 . Centrality of terms
As it could be expected, COVID-19 involves a wide variety of terms, concepts, references, citations and actions which, at first thought, could seem impossibly to clasify or organize in a coherent way, given the multidisciplinary character of the problem. However, as it can be appreciated in Figure 8 , it is possible to structure all the information available into few conceptual clusters, which can be, in turn, summarized by basically 3 concepts, namely "China", "Coronavirus" and "Disease outbrake". This has been consistent in the last 17 years,which allows to understand why it not should be surprising that a "Coronavirus outbrake took place in China". Finally, if we redraw that data of Figure 8 into a conceptual map, Figure 9 , which corresponds to the actual Conceptual Map of COVID-19, which allows to identify the modularity of the nodes that represent the entire database built from the scientific information available. 
A translational knowledge map of COVID-19 was constructed from the available literature data on articles, reports and patents, aiming to provide a relevant vision of the current stage and an overview of the knowledge structure of COVID-19.
Furthermore, this methodology has the potential to become a useful assessment tool for monitoring the evolution of knowledge on various emerging disease such as COVID-19. Finally, it should be mentioned that, despite that the fundamental scientific fact is that COVID-19 outbreak began affecting China, and therefore these terms are at the center of the conceptual structure, there exist less visible nodes identified by terms such as: "war", "biotechnology", "fear", "economic" and "economy", which appear related to patents (11).