Bibliometrics
has been defined as the use of statistical methods to analyze the mass of
literature to identify the historical development of a field of research [2-4],
as well as qualitative and quantitative research of publications. Bibliometric
studies were carried out in many areas of medicine, such as ophthalmology [5],
rheumatology [6], otolaryngology [7], nephrology [8], geriatrics [9],
4P-medicine [10], etc.
In
foreign scientific literature, PubMed is often used for bibliometric analysis
of medical publications. At the same time, bibliometric analysis considers the
total (absolute) number of publications, while calculating the relative
indicators, their dynamics for different periods of time, the amount of research
funding. One of the areas of bibliometric analysis is content analysis, which
includes identifying research trends [11-13].
In
recent years, many bibliometric studies have been published in highly rated
medical journals [14-18].
Chinese
scientists analyzed publications about COVID-19 obtained from a PubMed search
using the keyword "COVID-19". The authors reviewed 183 publications
published from January 14 to February 29, 2020. The following parameters were
analyzed: citizenship of researchers, place of work (hospitals, universities,
research institutions), journals, types of research [19].
We
conducted a bibliometric analysis of publications in the field of coronavirus
research to identify the main areas of research, collaboration of scientists
and organizations, and international research interaction.
As in
the previous work [18], the Dimensions [21] and PubMed databases were used to
search for documents.
On
the Dimensions platform, documents were searched using the keywords
"coronavirus" in titles and annotations for the period of 2019-2020.
We retrieved data on 2500 publications that were most relevant to the query.
A
search query on the NCBI (National Center for Biotechnology Information) PubMed
database included the term "coronavirus" in a search box using MeSH
(Medical Subject Headings). Information was retrieved on 17,140 works available
in PubMed (request date 10.04).
To
visualize bibliometric networks, the VOSviewer 1.6.13 software [22] was used,
which supports overlay and density visualizations [23].
In
this work, we used the methods of regression analysis of data [24] to analyze
the dependences of citation on various factors [25,26]. The models were built
using the OLS (Ordinary Least Squares) method to estimate the possible
parameters of a linear regression model, and a statistical prediction method
was also used.
Analysis
of the dynamics of publications from the PubMed database (Fig. 1) showed bursts
of research activity timed to coincide with outbreaks of coronavirus
infections. The first case of SARS-CoV virus disease was registered in 2002; an
outbreak of MERS-CoV Middle East respiratory syndrome in 2015; the SARS-CoV-2
virus, which is responsible for the new type of pneumonia pandemic COVID-19,
was identified in 2020 [27].
Figure: 1. Dynamics of
publications on the topic of "coronavirus" in the PubMed database for
the period of 1990-2020
The
study of the thematic orientation of publications from the PubMed database was
carried out using the VOSviewer program to build a terminological map based on
the joint occurrence of terms in the titles and annotations of articles. A
limitation was set: the term must occur at least 15 times. There were selected
131 terms out of 2499. The terminological map was built using the author's
keywords included 6 clusters, uniting 116 key concepts by thematic proximity
(Fig. 2).
Figure: 2. The most
frequent author's keywords (terms) in the publications related to research in
the field of "coronavirus" in the PubMed database, grouped into
clusters
This
map (Fig. 2) visualizes the frequency of use of author's terms and various
variants of term combinations both within clusters and between clusters.
The
size of the term circle reflects the number of publications in which the term
was found, and the distance between the two terms gives an approximate
indication of the relationship between the terms. The greater the number of
publications in which both terms were found, the stronger the relationship
between the terms. Clusters represent groups of terms that are relatively
strongly related to each other [28].
The
“red” cluster is the largest. It contains 47 terms. This cluster has received
the provisional name - "Genesis and Detection of Previous Types of
Coronavirus", as it combines concepts related to:
˗
previously known varieties of coronavirus and animal carriers (Porcine Epidemic
Diarrhea Virus), PEDV, bovine coronavirus, chicken, feline coronavirus, feline
infectious peritonitis, pigs, porcine delta coronavirus, porcine
gastrointestinal coronavirus, porcine transmissive virus (TGEV), swine flu);
˗
types of analysis (immunohistochemistry, PCR, phylogenetic analysis, RT-PCR,
serology, ELISA (enzyme-linked immunosorbent assay));
˗
vaccines and immunity (antibody, coinfection, immune response, immunity,
vaccine).
This
(the first) cluster also includes general medical terms (apoptosis, diagnosis,
diarrhea, isolation, mortality, pathogenesis, etc.).
The
second cluster ("green") unites 18 terms and can be called
"MERS-CoV", since the main terms refer to this coronavirus (camel,
coronaviruses, dromedary camels, Hajj, MERS, MERS-CoV, Middle East, Middle East
respiratory syndrome, Saudi Arabia, viruses, zoonoses).
The
third cluster (“blue”) includes 16 terms and can be designated as “epidemiology
and virology” (chloroquine, coronavirus infections, diseases, epidemiology,
infection, infection control, infectious diseases, infectious diseases,
inflammation, novel coronavirus pneumonia, outbreak, pandemic , public health,
quarantine, virology).
The
fourth cluster ("yellow") includes 15 terms and can be called
"COVID-19" (2019 novel coronavirus, 2019-NCoV, clinical
characteristics, coronavirus disease, COVID-19, novel coronavirus, pneumonia,
SARS-CoV-2, etc. .).
The
fifth cluster ("purple") includes 14 terms and can be called
"SARS" (coronavirus, influenza, influenza virus, interferon,
respiratory infection, respiratory syncytial virus, respiratory virus, SARS,
SARS coronavirus, SARS-CoV, severe acute respiratory syndrome (Severe Acute
Respiratory Syndrome).
The
sixth cluster (“pink”) “Wuhan” includes 6 terms and refers to the outbreak of
coronavirus in Wuhan (2019 novel coronavirus disease, China, zoonoses, Wuhan,
severe acute respiratory syndrome coronavirus 2, respiratory infections).
VOSviewer
also can display the time of occurrence of the most frequently used terms in
research. The closer to blue, the older the research, the closer to yellow, the
more modern the research. The results of the visual display of the temporal
updating of the terms reflects the sequence of the development of
epidemiological situations in connection with the varieties of coronavirus
(Fig. 3). The cluster under the code name "Genesis and Detection of
Previous Types of Coronavirus" was formed by 2016. Then were formed the
clusters: "MERS-CoV", "SARS", "Epidemiology and
Virology". The most recent emerging clusters were COVID-19 and Wuhan.
Figure: 3. The most
common terms in publications related to research in the field of
"coronavirus" on the PubMed database, time of appearance
VOSviewer
can visualize keyword density (Fig. 4). The color of each node in the keyword
density renderer depends on the density of the elements in that node. In other
words, the color of a node depends on the number of elements in the vicinity of
the node. Keywords in the yellow area appear more frequently; instead, keywords
in the green area appear less frequently. It can be seen that the most
frequently used keywords are "coronavirus" and "covid-19".
Figure: 4. The most
common terms used in the field of "coronavirus" on the PubMed base. (VOSviewer
density visualization)
To
analyze citation and co-authorship networks, we used a selection of scientific
papers from the Dimensions database.
To
identify the most cited publications, a restriction was set: the publication
must be cited at least 5 times. Out of 2500 articles, 493 were selected. As a
result, 28 clusters were identified (Fig. 5), uniting 420 publications (the
rest of the publications was not included in the clusters).
Figure: 5. The most cited publications
on the topic of "coronavirus" for 2019-2020 on the Dimensions database
The large number
of clusters shows a variety of research areas. Large nodes represent
influential publications. With the Fig. 5, the most cited articles are much
superior to others in importance; for the subsequent analysis, we selected
publications with more than 100 citations (there were 25 of them). We presented
these publications in the form of density visualization (Fig. 6).
Figure: 6. The most frequently cited
publications in the field of "coronavirus" for 2019-2020 (Dimensions Database
Density Visualization)
Figure 6
demonstrates that researchers more often refer to the following works:
"Clinical Features of Patients Infected with 2019 Novel Coronavirus in
Wuhan, China" [29]; "A Novel Coronavirus from Patients with Pneumonia
in China, 2019" [30]; “Early Transmission Dynamics in Wuhan, China, of
Novel Coronavirus – Infected Pneumonia” [31]; Epidemiological and Clinical
Characteristics of 99 Cases of 2019 Novel Coronavirus Pneumonia in Wuhan,
China: a Descriptive Study [32]; Clinical Characteristics of 138 Hospitalized
Patients with 2019 Novel Coronavirus – Infected Pneumonia in Wuhan, China [33].
Authors of the
article [29] represent a large number of specialized medical institutions in
China, including hospitals in Wuhan (The Central Hospital of Wuhan, Zhongnan
Hospital of Wuhan University, Yin-tan Hospital), medical universities and
colleges in China (Capital Medical University, Peking University Joint Center
for Life Sciences, Peking Union Medical College Tsinghua University School of
Medicine (Beijing); Huazhong University of Science and Technology (Wuhan);
hospitals (China-Japan Friendship Hospital, Beijing Ditan Hospital, Peking
University First Hospital, Peking University People's Hospital (Beijing) );
research centers (Center of Respiratory Medicine, National Clinical Research
Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese
Academy of Medical Sciences, Clinical and Research Center of Infectious
Diseases etc.).
The article
reports on the epidemiological, clinical, laboratory and radiological
characteristics, treatment, and clinical outcomes of 41 patients with
laboratory-confirmed 2019-nCoV infection hospitalized in Wuhan by January 2,
2020. Researchers communicated directly with patients or their families to
clarify epidemiological data and data about the symptoms [29].
The authors of
the article "A Novel Coronavirus from Patients with Pneumonia in China,
2019" [30] present: Beijing (NHC Key Laboratory of Biosafety, National
Institute for Viral Disease Control and Prevention, Chinese Center for Disease
Control and Prevention, and the Department of Infectious Diseases, Beijing
Ditan Hospital, Capital Medical University); Wuhan (Wuhan Jinyintan Hospital,
the Division for Viral Disease Detection, Hubei Provincial Center for Disease
Control and Prevention, the Center for Biosafety Mega-Science, Chinese Academy
of Sciences); Jinan (the Shandong First Medical University, Shandong Academy of
Medical Sciences).
The article
reports on a novel coronavirus that was detected in hospitalized patients in
Wuhan, China in December 2019 and January 2020.The beta coronavirus was
detected by unbiased sequencing in samples from patients with pneumonia.
Epithelial cells of the human respiratory tract were used to isolate a new
coronavirus named 2019-nCoV [30].
The authors of
the article "Early Transmission Dynamics in Wuhan, China, of Novel
Coronavirus – Infected Pneumonia" [31] represent different provinces of
China: Hubei (Hubei Provincial Center for Disease Control and Prevention,
Jingzhou Center for Disease Control and Prevention); Sichuan; Hunan; Henan;
Inner Mongolia; Liaoning; Guizhou; Jiangxi. The authors also represent Beijing,
Shanghai, Hong Kong.
The article
analyzed data on the first 425 confirmed cases of new coronavirus infection in
Wuhan as of January 22, 2020, to determine the epidemiological characteristics
of the disease. The authors described the characteristics and estimated the
epidemic doubling time and baseline reproductive number. The authors have shown
that human-to-human transmission has occurred among close contacts since
mid-December 2019 [31].
The authors of
the article [32] mainly represent Wuhan institutions (different departments of
Wuhan Jinyintan Hospital, State Key Laboratory of Virology, State Key
Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety
Mega-Science, Chinese Academy of Sciences); as well as Shanghai (Ruijin
Hospital, etc.).
The authors
sought to clarify the epidemiological and clinical characteristics of 2019-nCoV
pneumonia. In this retrospective study, they included all confirmed cases of
2019-nCoV at Wuhan Jinyintan Hospital from January 1 to January 20, 2020.
Epidemiological, demographic, clinical and radiological features and laboratory
data were analyzed. Of the 99 patients with 2019-nCoV pneumonia, 49% had a
history of eating seafood from the Huanan market. The authors suggested that
2019-nCoV infection is more likely to affect older men with underlying medical
conditions and can lead to severe and even fatal respiratory diseases such as
acute respiratory distress syndrome [32].
All authors of
the article “Clinical Characteristics of 138 Hospitalized Patients With 2019
Novel Coronavirus-Infected Pneumonia in Wuhan, China” [28] represent Zhongnan
Hospital of Wuhan University, Wuhan, Hubei.
The authors
describe the epidemiological and clinical characteristics of Novel
Coronavirus-Infected Pneumonia (NCIP) for a series of 138 consecutive
hospitalized patients with confirmed NCIP at Zhongnan Hospital of Wuhan
University, from January 1 to 28, 2020. Hospital transmission has been suspected
as a putative transmission mechanism for those infected medical workers and
hospitalized patients. Coronavirus transmission, presumably related to
hospitalization, was suspected in 41% of patients [33].
To build a
citation network by sources, we applied a restriction so that the source had at
least 7 publications. 74 out of 743 sources satisfied the condition. 70 sources
were identified in 3 clusters (Fig. 7).
Figure: 7. The most frequently cited
sources in the field of "coronavirus" for 2019-2020 on the Dimensions
Database
The clustering
results are shown in Table 1.
Table 1 - The
most frequently cited sources in the field of "coronavirus" for
2019-2020 on the Dimensions Database
|
¹
|
Source
|
Number of publications
|
Number of citations
|
Norm. number of
citations
|
Country
|
Impact Factor
|
|
1
êëàñòåð
|
|
1
|
American Journal of Roentgenology
|
13
|
42
|
5.7751
|
USA
|
3.1610
|
|
2
|
American Journal of Transplantation
|
13
|
11
|
1.5125
|
USA
|
6.4930
|
|
3
|
British Journal of Anaesthesia
|
7
|
2
|
0.275
|
United Kingdom
|
6.1990
|
|
4
|
BioScience Trends
|
9
|
151
|
20.7629
|
Japan
|
1.6860
|
|
5
|
Chinese Journal of Contemporary Pediatrics
|
12
|
10
|
1.375
|
China
|
0.1900
|
|
6
|
Chinese Journal of Epidemiology
|
7
|
118
|
16.2253
|
China
|
0.9048
|
|
7
|
Chinese
Journal of P
ediatrics
|
13
|
22
|
3.0251
|
China
|
0.2100
|
|
8
|
Chinese Journal of Preventive Medicine
|
9
|
4
|
0.55
|
China
|
0.7737
|
|
9
|
European
Journal of Nuclear Medicine and Molecular Imaging
|
7
|
26
|
3.5751
|
Germany
|
7.2770
|
|
10
|
Frontiers of Medicine
|
9
|
26
|
4.3277
|
China
|
1.8470
|
|
11
|
International Journal of Antimicrobial Agents
|
15
|
122
|
16.7753
|
Netherlands
|
1.5310
|
|
12
|
International Journal of Biological Sciences
|
11
|
9
|
1.2375
|
Canada
|
4.0670
|
|
13
|
International Journal of Environmental Research and Public
Health
|
15
|
15
|
2.4388
|
Switzerland
|
2.4680
|
|
14
|
Journal
of the American Medical Association (JAMA)
|
20
|
1073
|
147.5402
|
USA
|
51.2730
|
|
15
|
JMIR Public Health and Surveillance
|
12
|
0
|
0
|
Canada
|
2.3900
|
|
16
|
Canadian Journal of Anesthesia / Journal canadien
d'anesthésie
|
10
|
51
|
7.0126
|
Canada
|
2.1270
|
|
17
|
Journal of Clinical Medicine
|
19
|
178
|
24.4754
|
Switzerland
|
5.6880
|
|
18
|
Journal of Korean Medical Science
|
17
|
75
|
10.3127
|
South Korea
|
1.7160
|
|
19
|
Journal of Medical Virology
|
108
|
731
|
101.5178
|
USA
|
2.0490
|
|
20
|
Korean Journal of Radiology
|
21
|
29
|
3.9876
|
South Korea
|
3.7300
|
|
21
|
MMW Fortschritte der Medizin
|
3
|
0
|
0
|
Germany
|
0.0300
|
|
22
|
Morbidity and Mortality Weekly Report (MMWR)
|
8
|
70
|
9.6252
|
USA
|
14.8740
|
|
23
|
The
New England Journal of Medicine
|
21
|
2386
|
328.08100
|
USA
|
70.6700
|
|
24
|
PLoS ONE
|
10
|
10
|
2.2531
|
USA
|
2.7760
|
|
25
|
Radiology
|
26
|
593
|
81.539
|
USA
|
7.6080
|
|
26
|
British Medical Journal
(BMJ)
|
50
|
139
|
19.1128
|
United Kingdom
|
27.6040
|
|
27
|
The
Lancet
|
51
|
3275
|
450.3207
|
United Kingdom
|
59.1020
|
|
28
|
The
Lancet
Respiratory Medicine
|
12
|
179
|
24.6129
|
United Kingdom
|
22.9920
|
|
29
|
World
Journal of Pediatrics
|
7
|
63
|
8.6627
|
Germany
|
1.1690
|
|
30
|
中
华结
核和呼吸
杂
志
|
33
|
40
|
5.5001
|
China
|
2.0360
|
|
2
êëàñòåð
|
|
31
|
Antiviral Research
|
8
|
44
|
7.5553
|
Netherlands
|
4.1300
|
|
32
|
Archives
of Virology
|
8
|
4
|
1.0518
|
Germany
|
2.1340
|
|
33
|
Communicable diseases intelligence (Comm Dis Intell)
|
8
|
7
|
0.9625
|
Australia
|
1.0000
|
|
34
|
Emerging
Microbes & Infections
|
26
|
71
|
12.5223
|
United Kingdom
|
6.2120
|
|
35
|
Eurosurveillance
|
35
|
436
|
60.3274
|
France
|
7.4210
|
|
36
|
Frontiers in Microbiology
|
8
|
43
|
11.3064
|
Switzerland
|
4.2590
|
|
37
|
Infection, Genetics and Evolution
|
8
|
57
|
9.8446
|
Netherlands
|
2.6110
|
|
38
|
Journal of Microbiology and Biotechnology
|
8
|
8
|
2.3664
|
Netherlands
|
1.9750
|
|
39
|
Journal of Microbiology, Immunology and Infection
|
13
|
42
|
8.2838
|
Taiwan
|
2.4550
|
|
40
|
Journal of Virology
|
30
|
201
|
38.551
|
USA
|
4.3240
|
|
41
|
Methods in molecular biology
|
12
|
3
|
0.4125
|
USA
|
10.7100
|
|
42
|
Nature
|
48
|
606
|
83.3265
|
United Kingdom
|
43.0700
|
|
43
|
Pathogens
|
8
|
11
|
1.638
|
Switzerland
|
3.4050
|
|
44
|
Poultry Science
|
8
|
5
|
1.3147
|
USA
|
2.0270
|
|
45
|
Proceedings
of the
National Academy
of
Sciences
of the
United States
of
America
|
7
|
64
|
11.6852
|
USA
|
9.5800
|
|
46
|
Science
|
11
|
210
|
28.8755
|
USA
|
16.8100
|
|
47
|
Journal of Infectious Diseases
|
10
|
44
|
8.4334
|
Netherlands
|
5.0450
|
|
48
|
Transboundary and Emerging Diseases (Transbound Emerg Dis)
|
13
|
44
|
11.5693
|
Germany
|
1.9500
|
|
49
|
Veterinary Microbiology
|
32
|
96
|
18.8449
|
Netherlands
|
2.7910
|
|
50
|
Veterinary Record
|
8
|
12
|
3.0298
|
United Kingdom
|
2.0500
|
|
51
|
Virology
|
15
|
3
|
0.7888
|
Germany
|
2.6570
|
|
52
|
Virus Research
|
15
|
21
|
5.3963
|
Netherlands
|
2.7360
|
|
53
|
Viruses
|
9
|
10
|
2.0022
|
Switzerland
|
3.8110
|
|
3
êëàñòåð
|
|
54
|
Chinese Medical Journal
|
29
|
74
|
10.1752
|
China
|
1.0530
|
|
55
|
Chung-Hua Wai Ko Tsa Chin (Chinese Journal of Surgery)
|
8
|
2
|
0.275
|
China
|
|
|
56
|
Clinical Infectious Diseases
|
37
|
193
|
28.1687
|
United Kingdom
|
9.1170
|
|
57
|
Emerging
Infectious Diseases
|
32
|
279
|
44.3841
|
USA
|
7.4220
|
|
58
|
Epidemiology and Infection
|
10
|
33
|
7.4226
|
United Kingdom
|
2.0750
|
|
59
|
Gut
|
7
|
7
|
0.9625
|
United Kingdom
|
17.9430
|
|
60
|
Infection
Control & Hospital Epidemiology
|
12
|
19
|
3.8669
|
United Kingdom
|
3.0840
|
|
61
|
International Journal of Infectious Diseases
|
33
|
329
|
45.3638
|
Netherlands
|
3.5380
|
|
62
|
Journal
of Clinical Microbiology
|
6
|
13
|
1.7875
|
USA
|
4.9590
|
|
63
|
Journal of Hospital Infection
|
16
|
99
|
16.4978
|
United Kingdom
|
3.7040
|
|
64
|
Journal of Infection
|
30
|
117
|
16.0878
|
Italy
|
5.0990
|
|
65
|
Journal of Infection and Public Health
|
12
|
14
|
3.1794
|
Netherlands
|
2.4870
|
|
66
|
Journal of Travel Medicine (J Travel Med)
|
13
|
171
|
23.5129
|
United Kingdom
|
4.1550
|
|
67
|
Microbes and Infection
|
12
|
3
|
0.4125
|
Netherlands
|
2.6690
|
|
68
|
The Lancet Infectious Diseases
|
7
|
53
|
7.2876
|
United Kingdom
|
27.5160
|
|
69
|
Travel Medicine and Infectious Disease
|
3
|
8
|
0.6634
|
Netherlands
|
4.8680
|
The parameters
given in the table indicate the number of cited publications of the source, the
number of citations of the publications of the source (normal and normalized),
the country of origin of the source, and the impact factor of the journal. The
normalized document citations are calculated as the ratio of the number of
citations per document to the average number of citations for all documents
published in the same year and included in the data provided by VOSviewer. The
normalization is intended to correct the fact that older publications took
longer to receive citations than more recent publications.
As seen from
Fig. 7, the first cluster includes the most recent cited sources (late 2019 -
early 2020), the third - those cited in the mid-2019, and the second - those
cited at the beginning of 2019.
Fig. 8 shows
that the first cluster is dominated by journals published in the USA, in the
second cluster - in the USA and the Netherlands, in the third cluster - in the
UK and in the Netherlands.
Figure: 8. Number of sources by cluster
(in%), considering the country publishing the journal
The average
impact factor of journals in the 1st cluster is 10.48, the second - 6.22, and
the third - 6.65.
We excluded sources
with a normalized number of citations less than 10 from further consideration.
The absolute
leader in citation is The Lancet (450,3207). In second place is The New England
Journal of Medicine (328,081). In third place is the Journal of the American
Medical Association (JAMA) (147.5402). In fourth place is the Journal of
Medical Virology (101.5178). The rest of the sources can be divided into groups
(sources are listed in descending order):
- from 100 to 50
(Nature, Radiology, Eurosurveillance);
- from 50 to 30
(International Journal of Infectious Diseases, Emerging Infectious Diseases,
Journal of Virology);
- from 30 to 20
(Science, Clinical Infectious Diseases, The Lancet Respiratory Medicine,
Journal of Clinical Medicine, Journal of Travel Medicine, BioScience Trends);
- from 20 to 10
(British Medical Journal, Veterinary Microbiology, Chinese Journal of
Epidemiology, International Journal of Antimicrobial Agents, Journal of
Hospital Infection, Journal of Infection, Emerging Microbes & Infections,
Proceedings of the National Academy of Sciences of the United States of
America, Transboundary and Emerging Diseases, Frontiers in Microbiology,
Journal of Korean Medical Science, Chinese Medical Journal).
There are 8 of
the 28 highly cited journals published in the USA, 8 - in the UK, 3 - in the
Netherlands, 1 - in France, 2 - in Switzerland, 1 - in Japan, 2 - in China, 1 -
in Italy, 1 - in Germany, 1 - in South Korea.
To find the most
cited authors, we applied the search term: the author must have at least 10
publications. There were 16 such authors out of 11035. These results were
presented in the form of density visualization (Fig. 9). Authors in yellow
areas are cited more often; on the contrary, authors in green areas are cited
less frequently.
Figure: 9. The most frequently cited
authors in the field of "coronavirus" for 2019-2020 on the Dimensions
Database (Density visualization)
The most cited
authors are Christian Drosten (German virologist specializing in coronaviruses;
head of the Berlin Charité Institute of Virology), Kwok-Yung Yuen (Hong
Kong microbiologist, physician and surgeon), Alimuddin Zumla (British Zambian
professor of infectious diseases and international health at the Medical
University of London University College), Stanley Perlman (Professor of
Microbiology and Immunology, Professor of Pediatrics, University of Iowa),
Lanying Du (Head of Viral Immunology Laboratory, Lindsley F. Kimball Research
Institute), Shibo Jiang (Shanghai School of Medicine, Fudan University in
Shanghai, China), Fang Li (Associate Professor, Department of Veterinary and
Biomedical Sciences, University of Minnesota), Ziad A. Memish (Undersecretary
of Public Health in Saudi Arabia), Jaffar A. Al-Tawfiq (Adjunct Professor of
Medicine, Indiana University School of Medicine).
For the
analysis, the following parameters were identified (Table 1):
PubNum
-
the number of publications,
CiteNum
- the number of citations,
Country
- the country of publication,
ImpactFactor
- the impact factor of the
publication. All of the three analyzed clusters were combined. As a result of
the study, 3 models were built, and a forecast of publication activity was
made, depending on many factors.
A model 1 was
built. OLS method was used for the analysis. The analysis was carried out by
country, considering countries, the number of citations, and the impact factors
of journals. Dependent variable is
PubNum, regressors:
CiteNum,
Country,
ImpactFactor. The following regression equation was built:
|
PubNum = 18.3 +
0.02 * CiteNum - 0.47 * Country - 0.35 * ImpactFactor
|
(1)
|
The most
significant variable in this equation is the
CiteNum
variable. It is
significant even at the 1% significance level. The highest P-value
(significance level, extreme value of the statistic) is obtained for the
Country
variable. As a result, it is difficult to distinguish how significant
indicators are on the number of publications by country.
Then a model 2
was built. In this model, the number of citations,
CiteNum, was chosen
as the dependent variable; the analysis was carried out taking into account
countries, impact factors of journals, and the number of publications.
Country
is significant at 10% significance level,
ImpactFact
and
PubNum
are significant at 1% significance level. As a result, the following regression
equation was obtained:
|
CiteNum = -234 + 12.52 * Country + 28.94
* ImpactFact + 7.67 * PubNum
|
(2)
|
It can be
concluded that the number of citations depends on the country of publication,
the impact factor of the journal and the number of publications. At the same
time, it is the impact factor of the journal that influences the number of
citations to a greater extent, to a lesser extent - the country of publication,
and even to a lesser extent - the number of publications in the journals of
this country.
And then a model
3 was built. By removing the
Country
regressor (as insignificant), the
following regression equation was obtained for the model:
|
CiteNum = -170 +
28 * ImpactFact + 7.56 * PubNum
|
(3)
|
The country of
publication matters, but not as significant as the impact factor of the
journal.
Based on the
results of the model of the dependence of the number of citations on the
selected regressors, a forecast for the dependent variable
CiteNum
was
built.
Figure: 10. Citation forecast depending
on the impact factor of the publication
The figure shows
the actual values of the number of citations and the citation
forecast.
The figure shows that, depending on several factors, the predicted values are
somewhat smoothed. Most of the publications is with an impact factor in the
range from 15 to 30. The result is not very much dependent on the country of
publication and the number of publications.
The values of
the predictive function are combined on a single graph with the most
significant actual parameters, which made it possible to determine the most
preferable values of the "impact factor" parameter to maximize the
number of citations.
This study
performed bibliometric analysis and visualization of scientific publications
related to the novel and previous coronavirus types. The results obtained can
be summarized as follows.
Obvious bursts
of publication activity in the biomedical literature are associated with
epidemics of the previously known coronaviruses SARS-CoV, MERS-CoV and the new
type of SARS-CoV-2.
Clustering of
author's keywords of publications in the PubMed database showed that by now a
large cluster of terms has been formed, which can be conventionally called
“Genesis and detection of previous types of coronavirus”. The cluster includes
specific concepts related to animal carriers of different types of coronavirus;
terms denoting modern methods of analysis in the field of virology; terms
related to vaccines, viruses, and immunity.
Clusters related
to MERS-CoV and SARS have been formed. Currently, clusters are being formed,
conventionally called by us "COVID-19" and "Wuhan". The
first of them includes terms close to clinical manifestations. The second is
more geographic. The most common keywords in PubMed are “coronavirus” and “covid-19”.
We conducted a
citation analysis based on publication activity during 2019-2020 on the subject
of "coronavirus" based on the Dimensions database.
Of the most
cited 25 articles, 8 were published in The Lancet (UK), 5 in the New England
Journal Of Medicine (USA), and 2 in Nature (UK). One highly cited article was
published by: JAMA (USA), International Journal of Infectious Diseases
(Netherlands), Journal of Virology (USA), Nature Reviews Microbiology (UK),
Eurosurveillance (France), Science (USA), The Lancet Respiratory Medicine UK) ,
Journal of Medical Virology (USA).
Consequently, 12
of the 25 most cited publications are published in journals published in the
UK, 9 in the USA. Although, as further analysis showed, the authors of the most
cited publications are Chinese doctors and researchers.
The authors of
the most cited articles represent a large number of specialized medical
institutions in China, including hospitals and research centers in Wuhan,
medical universities and scientific organizations in various provinces of
China, as well as centers in Beijing, Shanghai, and Hong Kong.
The articles
report:
- on the
epidemiological, clinical, laboratory and radiological characteristics and
treatment and clinical results of 41 patients with laboratory-confirmed
2019-nCoV infection hospitalized in Wuhan by January 2, 2020 [29];
- on the
isolation of a new coronavirus from the epithelial cells of the respiratory
tract of hospitalized patients in Wuhan, China [30];
- on evidence of
human-to-human transmission of the virus [31];
- on the
clarification of the epidemiological and clinical characteristics and
assessment of the predisposition of populations to pneumonia 2019-nCoV [32];
- on the
epidemiological and clinical characteristics of the disease and the likelihood
of nosocomial transmission [33-35].
The most cited
journals on the subject of "coronavirus" are (listed in descending
order of citation):
- The Lancet
(which published 8 of the 25 most cited articles);
- The New
England Journal of Medicine (which published 5 of the 25 most cited articles);
- Journal of the
American Medical Association (JAMA) (which published 1 article out of 25 most
cited).
The top 10 cited
journals also include: Journal of Medical Virology; Nature; Radiology;
Eurosurveillance; International Journal of Infectious Diseases; Emerging
Infectious Diseases; Journal of Virology; Science; Clinical Infectious
Diseases; The Lancet Respiratory Medicine.
A temporal
analysis of citation showed that during the development of the coronavirus
pandemic, journals published in the United States were most actively cited. In
2019, magazines published in the Netherlands and the United Kingdom were widely
cited before the pandemic developed.
The most cited
authors include specialists in the field of virology, immunology, medicine,
working in Europe, Hong Kong, USA. So, the most cited author is a German
virologist specializing in coronaviruses, Christian Drosten. The top 10 cited
authors include only one researcher from China (Shibo Jiang, Shanghai Medical
School, Fudan University in Shanghai). It is worth noting that the top 10 authors
include Deputy Minister of Health of Saudi Arabia Ziad A. Memish.
In the future,
we plan to develop a methodology for visualizing scientific data using the
following methods and programs:
- the author's
method for calculating the semantic similarity of documents,
-
"t-SNE" method for calculating 3D coordinates of terms using a
similarity matrix,
- automatic
programs for detecting semantic similarities between terms and creating lines
between spheres,
- WebVR methods
for 3D visualization using calculated data.
We used a
similar approach to visualize the three-dimensional cyberspace of scientific
works [8].
Multivariate
spatial analysis of publications was carried out. When constructing a forecast,
a non-standard approach was used, which is based not on time series, as is
usually the case in forecasting, but on the estimation of interval maximization
of parameters. A forecast of the most probable maximum citation values was
constructed depending on several factors, including the most significant one -
the impact factor of the journal, as it turned out.
The reported
study was funded by RFBR, project numbers 18-07-00225, 18-07-00909,
18-07-01111, 19-07-00455, and 20-04-60185.
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