The results section is intentionally kept brief, as results will evolve as more research becomes available and will be reported to greater extent on the associated website: http://covid19biblio.com/ The results presented in this study are as of March 23rd, 2020.
Keyword co-occurrence analysis
There were 225 keywords that occurred in three or more manuscripts. These divided into four clusters. These are shown in the keyword co-occurrence network diagram (Fig. 1a). While the contents of the clusters are likely to develop in future analysis the current clusters seem to represent the following (see Fig. 1a for colour reference):
Cluster 1 (red) relates to “Health and pandemic management”, and include topics like the pandemic, the impact on global health, the disease outbreak, infection control and travel. Within this cluster, we found the topic of “Global health politics”, which in addition to terms on the pandemic and the impact on global health included terms like public health, disease transmission and health planning. Further, within this cluster, we found the topic of “Pandemic politics”, which in addition to the terms disease outbreak and infection control included terms like mass screening and health personnel.
Cluster 2 (green) related to “The disease and its pathophysiology” of COVID-19, including topics like its genome and its relationship with SARS, but also topics like epidemiology and its outbreak and that it is a zoonosis. Within this cluster, we found the topic of “Viral biology”, which in addition to terms on its genome and SARS, included terms like phylogeny and disease reservoirs. Further, within this cluster, we find the topic of “Viral spread”, which in addition to the term epidemiology, includes terms like quarantine, importation and incubation period. Lastly, this cluster contained the topic “Basic clinical medicine”, which in addition to the terms outbreak and zoonosis, included terms like transmission and mortality.
Cluster 3 (blue) related to the “Clinical epidemiology of the disease”, including topics like age (aged, children, adolescent) and gender (male and female), risk factor, population surveillance and pregnancy. Only one major topic was identified within this cluster: “Clinical characteristics”. In addition to the terms on age and gender, this topic included terms like prognosis, myalgia, biomarkers and laboratory medicine.
Cluster 4 (yellow) related to “Treatment of the disease”, with terms like antiviral agents, diagnosis, ritonavir and drug combinations.
Four keywords act as bridging words in the keyword co-occurrence network graph, connecting the clusters and graph together to a high degree. These are: China, Betacoronavirus, Viral pneumonia and Coronavirus infection.
[Insert Fig. 1a about here]
Red cluster: Health and pandemic management; Green cluster: The disease and its pathophysiology; Blue cluster: Clinical epidemiology of the disease; Yellow cluster: Treatment of the disease. Size of circle shows the relative number of occurrences of a keyword, and weight of line indicates the frequency two keywords are linked. To examine how topics link to other topics within and across clusters, access the interactive map: http://covid19biblio.com/keyword-co-occurrence/.
[Insert Fig. 1b about here]
Displayed in the interactive tool, where “pneumonia” is selected to show other major keywords it is researched together with. The figure also shows that in addition to linking with keywords within its own cluster, the keyword “pneumonia” links with topics in the other clusters.
Bibliometric coupling analysis
Of the 411 articles available in Scopus, 280 of them included a reference list and were included in the bibliometric coupling analysis. The network graph subsequently comprised of four clusters, as shown in Fig. 2.
Cluster 1 (red) related to contributions giving an “Overview of the new virus” and included literature on a wide variety of topics like genomic characterization, pathology, diagnosis and treatment, recognition of this pandemic stemming from China, and first lessons from clinical management. The subgroup “Commentaries on the new virus” related to literature on the first cases, the infection being without borders, therapeutics and triage. Another subgroup, “Commentaries on the phenomenon”, included literature on the virus spread and the reproductively of the virus.
Cluster 2 (green) related to contributions to “Clinical medicine”, including topics such as transmission routes, epidemiology, clinical characteristics of patients (age, gender, fever, abdominal symptoms). One subgroup related to “Clinical medical characteristics”, with topics like those mentioned above. Another subgroup consisted of literature on “Endemic areas” such as China, Wuhan, Korea, Thailand and Africa. A smaller subgroup, “Comparisons with SARS”, consisted of literature comparing the current pandemic with SARS.
Cluster 3 (blue) “On the virus” consisted of literature on topics ranging from the origin of the virus, its genome, possible therapeutics and early research on these topics. A major subgroup in this cluster, “the virus’ origin and its genome”, included topics like zoonotic spillover and transmission, but also early literature on the outbreak of the pandemic. Another subgroup, “Possible therapeutics”, included literature on chloroquine, Chinese herbal medicine, anti-viral drugs and the ACE2 receptor. A third large subgroup, “Early research on genome and therapeutics” included literature on receptors, transmission, detection and vaccine.
Cluster 4 (yellow), was by far the smallest cluster. It consisted of literature on “Reproduction rate and spread”, but also covers literature on screening, the pandemic and treatment. While this cluster was less well defined in the present analysis, this may change as the corpus of published work grows in the coming weeks and months.
Accessing this network diagram online, allows the reader to search for articles by author, identify related studies, and directly access the article by clicking on it.
[Insert Fig. 2 about here]
Size of the circle shows the relative number of total links to other articles, the proximity between circles indicate similarity, as gauged by how many references articles share. The weight of the line indicates the number of shared references (set minimum to 4 for clarity). To examine how individual articles link to other articles, and adjust the resolution, access the searchable and interactive version of this map: http://covid19biblio.com/coupling-analysis/