Bibliometric Coupling Analysis of COVID-19 Literature

A Bibliographic coupling analysis examines the reference lists of documents, to identify where articles share a common reference. The degree of overlap between article’s reference list represents the strength of the connection between them. Greater overlap means two documents share a large proportion of references, and thus a probability that the content are on related topics. Conversely, little overlap indicates the documents are based on distinct pieces of literature, with few commonalities.

For more information about what a bibliometric coupling analysis is, please see the about page.

 

 

Applications for the bibliometric coupling results:

  • Organizes published articles by topics, enables the reader to identify articles within an area of interest.
  • It is a complementary way to identify related research. The results are not based on search terms, so identifying related articles through this method opens the possibility to make valuable and novel connections between research articles.

To access the interactive map, you need to open the graph in VosViewer, which allows you to search the map, see article information on each node in the graph diagram, and by clicking on the node, open the article in a browser window. To open the map in VOSviewer, please download the free program: VOSviewer here, and open the files.

Insights and trends

When we initiated this project in mid-March 2020, there were 296 documents on COVID-19 in Scopus, of which 197 included the bibliometric information necessary to conduct a coupling analysis. Since then, there has been a sharp increase, and in mid-September, 45638 documents on the pandemic was available in the database.

Coupling analyses on documents from early in the pandemic suggested four topics: 1) The contributions were on “Overview of the new virus”, 2) “Clinical medicine,”, 3) “On the virus,” which included topics ranging from the origin of the virus, its genome, possible therapeutics and early research on these topics, and 4) literature on “Reproduction rate and spread.”  For a more detailed description, please see our preprint

In July, we conducted a coupling analysis on journals, creating a network graph showing journals that have published ten or more articles on COVID-19 since March. Of the (then) 4585 journals that had published COVID-19 literature, 706 have published ten or more articles. The analyses showed 5 clusters. In the different clusters, these were the journals with the highest closeness centrality: 1) Diabetes And Metabolic Syndrome: Clinical Research And Reviews, BMJ, and Diabetes Research And Clinical Practice. 2) Science Of The Total Environment, International Journal Of Infectious Diseases, and Lancet. 3) Journal Of Medical Virology, Frontiers In Public Health, and Medical Hypotheses. 4) International Journal Of Environmental Research And Public Health, Sustainability, Brain, Behavior, And Immunity. 5) Jama – Journal Of The American Medical Association, Pediatric Pulmonology, and Frontiers In Pediatrics. For more details, see this post.

Network Visualization of Bibliometric coupling of COVID-19 meta-analysis

In this post, we analyze the 1622 COVID related articles published up to the 14th of July, with the term Meta-analysis in the title, available in Scopus. We conducted a keyword co-occurrence analysis, and a coupling analysis.

With the vast amounts of research on COVID, and the value in examining the results of multiple studies, meta-analysis are important studies, that generally yield more reliable results, as well as potentially identifying moderators and mechanisms single studies are ill-suited for.

In the keyword co-occurrence analysis, we se the terms group into 9 clusters. We include the top 10 keywords in each cluster in the table below.

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5
Keyword Weighted degree Occurrence Keyword Weighted degree Occurrence Keyword Weighted degree Occurrence Keyword Weighted degree Occurrence Keyword Weighted degree Occurrence
artificial ventilation 150 3123 viral pneumonia 415 6741 mortality 509 7898 disease severity 346 6797 virology 268 4516
mortality rate 140 2882 prevalence 307 4870 risk factor 299 5245 prognosis 167 3353 procedures 139 2039
treatment outcome 173 2709 clinical outcome 138 2572 comorbidity 226 4329 severity of illness index 156 2963 quality control 104 1914
antivirus agent 90 2084 risk assessment 130 2211 hypertension 168 3556 c reactive protein 92 2701 isolation and purification 102 1735
adult respiratory distress syndrome 86 2048 epidemiology 131 1771 diabetes mellitus 162 3353 blood 123 2533 nonhuman 75 1541
hydroxychloroquine 82 1855 age 89 1715 disease association 163 3100 d-dimer 76 2029 diagnosis 90 1472
drug efficacy 83 1715 sars 88 1442 outcome assessment 133 2450 procalcitonin 50 1556 children 82 1442
drug therapy 102 1500 cohort analysis 62 1252 cardiovascular diseases 111 2306 biological marker 67 1511 pathogenicity 64 1401
antiviral agents 73 1459 infection risk 70 1243 mortality risk 77 1429 laboratory test 51 1459 reverse transcription polymerase chain reaction 58 1100
lopinavir plus ritonavir 46 1447 medline 60 1190 retrospective study 72 1371 lactate dehydrogenase 48 1449 real time polymerase chain reaction 51 996
drug safety 68 1429 major clinical study 55 1074 newcastle-ottawa scale 74 1337 interleukin 6 56 1414 immunology 47 968
drug effect 71 1299 sex difference 52 1029 chronic obstructive lung disease 52 1315 metabolism 63 1360 genetics 43 850
corticosteroids 50 1241 adolescent 53 981 disease course 57 1178 disease exacerbation 61 1336 clinical laboratory techniques 36 816
azithromycin 46 1222 health care personnel 59 858 malignant neoplasm 45 1176 biomarkers 57 1253 laboratory technique 36 816
chloroquine 47 1080 practice guideline 43 836 very elderly 55 1106 lymphocyte count 45 1212 diagnostic accuracy 29 527
Cluster 6 Cluster 7 Cluster 8 Cluster 9
Keyword Weighted degree Occurrence Keyword Weighted degree Occurrence Keyword Weighted degree Occurrence Keyword Weighted degree Occurrence
intensive care units 219 4354 fever 124 3250 diarrhea 87 2179 pathophysiology 115 2198
complication 233 3744 clinical features 121 2951 aspartate aminotransferase 62 1759 headache 50 1319
hospitalization 178 3609 coughing 105 2724 alanine aminotransferase 58 1677 cerebrovascular disease 45 1087
hospital admission 121 2476 dyspnea 83 2409 vomiting 48 1314 cerebrovascular accident 30 682
incidence 130 2316 fatigue 67 1915 nausea 45 1231 hospital discharge 33 636
hospital mortality 69 1331 ct 83 1666 abdominal pain 42 1056 dizziness 18 555
hospital patient 64 1277 myalgia 53 1496 bilirubin 31 1004 anosmia 28 426
acute kidney failure 52 1167 lymphocytopenia 46 1399 gastrointestinal symptom 39 924 nausea and vomiting 15 407
intensive care 49 1001 pathology 75 1333 thrombocytopenia 37 850 neurologic disease 20 334
heart injury 39 947 pneumonia 57 1240 liver disease 36 808 smelling disorder 23 311
critical illness 44 779 sore throat 35 1168 anorexia 22 698 brain ischemia 13 273
invasive ventilation 31 690 diagnostic imaging 50 993 liver injury 31 650 brain hemorrhage 12 261
acute kidney injury 38 609 infant 40 953 aspartate aminotransferase blood level 17 561 ageusia 12 252
lung embolism 30 595 pregnancy 46 846 alanine aminotransferase blood level 15 503 olfaction disorders 17 219
heart arrhythmia 26 577 symptoms 37 786 gastrointestinal disease 27 486 taste disorder 13 216

 

 

The coupling analysis gives a similar clustering. If you open the map in VOSviewer, it is possible to click on individual studies, see which the are closely linked to, and open the paper directly in a browser.

 

VOSViewer files:

To open the map directly in VOSviewer, please use the coupling MAP and NET files from December 1st, available through the Open Science Foundation (OSF) repository:
https://osf.io/54gqw/

 

Network Visualization of Bibliometric Co-Citation Analysis of COVID-19 Literature, based on a sample of publications in January 2021

In this post, we analyze the newest 2000 articles available in Scopus at the start of feburary. we conducted a co-citation analysis, with minimum 50 citation of a given journal, with minimum of 500 co-citations between two journals to show the link.

VOSViewer files:

To open the map directly in VOSviewer, please use the coupling MAP and NET files from December 1st, available through the Open Science Foundation (OSF) repository:
https://osf.io/54gqw/

 

Network Visualization of Bibliometric Coupling Analysis of recent COVID-19 Literature, based on the publications the past week

Network Visualization of Bibliometric Coupling Analysis of recent COVID-19 Literature as it looks like December 1st, 2020

In this post, we analyze articles made available in the past week, with reference lists. There were 1918 new documents, of which 956 include the necessary bibliographic information for a coupling analysis. For the analysis, we used linlog and total link strength. The minimum cluster size was set to 20. There are 6 main clusters in the map.

VOSViewer files:

To open the map directly in VOSviewer, please use the coupling MAP and NET files from December 1st, available through the Open Science Foundation (OSF) repository:
https://osf.io/54gqw/