
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-07426
00016
10.1097/MD.0000000000039737
3
4900
Research Article
Systematic Review and Meta-Analysis
A bibliometric analysis of domestic and international research on hemorrhagic fever with renal syndrome over the past 2 decades
https://orcid.org/0009-0003-8066-544X
Zhou Wenfang MD 742838524@qq.com
a
Dong Yonghai MD a*
Liu Xiaoqing MD liux14@163.com
a
Ding Sheng MD jxcdccfs2@126.com
a
Si Hongyu MD sihongyutj@163.com
a
Yang Cheng MD 827414065@qq.com
a
a Jiangxi Provincial Key Laboratory of Major Epidemics Prevention and Control, Young Scientific Research and Innovation Team of Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, Jiangxi Province, China.
* Correspondence: Yonghai Dong, Jiangxi Provincial Key Laboratory of Major Epidemics Prevention and Control, Young Scientific Research and Innovation Team of Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, Jiangxi Province 330026, China (e-mail: dyhai123@126.com).
13 9 2024
13 9 2024
103 37 e3973730 6 2024
23 8 2024
27 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Background:

Bibliometrics and statistics were used to determine and analyze the research status of hemorrhagic fever with renal syndrome (HFRS) from 2004 to 2023, and objectively reflect the development and future trend of HFRS at home and abroad through comparison.

Methods:

To search the research literature on HFRS in China National Knowledge Network and Web of Science databases from January 2004 to December 2023, CiteSpace and VOSviewer were used to visually analyze the annual publication trends, authors, research institutions, countries, co-cited literature, keywords and other contents of the included literatures.

Results:

A total of 4460 Chinese literatures and 2372 foreign literatures were included. The number of HFRS published in the Web of Science database showed a trend of positive growth, while the number of HFRS published in China National Knowledge Network showed a trend of decline. Bai Xuefan and Wang Zhiqiang were the most published authors in China, and foreign scholars Vaheri, Antti, Ahlm, Clas. The main research institutions in the domestic literature were Zhejiang Provincial Center for Disease Control and Prevention, Liaoning Provincial Center for Disease Control and Prevention, while foreign research institutions concentrated on the University of Helsinki and Ministry of Health. The top 3 countries in the literature research of Web of Science are the USA, China, and Germany.

Conclusion:

The analysis results of hot spots and trends suggested that we need to develop more reliable tools and methods in the monitoring and spatio-temporal analysis of HFRS epidemic data in the future, so as to provide references for the surveillance and early warning of zoonotic diseases in the field of public health research.

bibliometric analysis
epidemic hemorrhagic fever
hemorrhagic fever with renal syndrome
HFRS
the National Natural Science Foundation of China82360661 Xiaoqing LiuJiangxi Provincial Key Laboratory of Major Epidemic Prevention and Control2024SSY06021 Sheng Dingthe Leading Medical Discipline (Epidemiology) of Jiangxi Province, ChinaSheng DingOPEN-ACCESSTRUE
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pmc1. Introduction

Hemorrhagic fever with renal syndrome (HFRS), also known as epidemic hemorrhagic fever, is a natural epidemic disease caused by Hantavirus and mainly hemorrhagic disease with mice as its source of infection.[1–3] The main clinical manifestations are fever, bleeding, congestion, hypotensive shock, and kidney damage.[4,5] Hantavirus is mainly transmitted by rodent excreta (urine, feces) or saliva, in the form of aerosols, and the population is generally susceptible.[6] The main source of infection is rodents, including Rattus norvegicus and Apodemus agrarius, which carry hantavirus and are transmitted to humans through their excretions or secretions.[7]

HFRS is distributed throughout the world, but China is the country with the largest number of cases worldwide, accounting for more than 90% of the reported cases in the world. In addition, HFRS has been reported in all 31 provinces in China,[8,9] showing its wide geographical distribution. In recent years, with the acceleration of urbanization, the expansion of the range of human activities, and the influence of climate change and other factors, the range and number of rodents may also change, thus affecting the prevalence of HFRS.[10] In the patient population, adults are the main group of patients, but special groups such as children, the elderly, and pregnant women may also be affected, and these groups should be individualized treatment according to the specific situation.[11] In general, HFRS is a disease with serious harm, and understanding its pathogenesis, population distribution, and harm will help to better prevent and control the disease.[12,13]

Bibliometrics has been widely used to explore knowledge structure and development trend through qualitative and quantitative analysis.[14,15] Two new types of bibliometrics software, VOSviewer and CiteSpace,[16] can not only provide researchers with a more convenient calculation method, but quickly evaluate the distribution of countries, institutions, authors, and journals in a specific research field,[17] but also grasp the hot spots and development trends in the research field.[18] Based on this, this study uses information visualization software VOSviewer and Citespace to integrate domestic and foreign literature data on HFRS research into a visual knowledge graph, aiming to analyze the hot spots and frontiers of research in this field, in order to provide data support for in-depth research on HFRS.

2. Materials and methods

2.1. Literature sources and search strategies

The Chinese data in this study came from China National Knowledge Network (CNKI), and the search strategy is as follows: Select advanced search and the search formula was SU = “Hemorrhagic fever with renal syndrome” + “Epidemic hemorrhagic fever.” The English data for this study came from Web of Science (WOS) Core Collection and the search formula was TS = (hemorrhagic fever with renal syndrome) OR TS = (HFRS) OR TS = (epidemic hemorrhagic fever). Inclusion criteria for literature search: Literature related to HFRS; Timespan: January 1, 2004 to December 31, 2023. Exclusion criteria for literature search: meeting abstracts; letters; editorial materials; corrections; proceeding papers; Duplicate literature. Two-person parallel data extraction and analysis were carried out, that is, 2 researchers independently searched and cross-checked literature data to visually analyze the included literature. A total of 5032 articles in Chinese and 2950 in foreign languages were obtained for the first time. After excluding irrelevant documents such as conferences, newspapers, and patents, and then using Citespace for literature duplicate checking, 4460 Chinese studies and 2372 foreign studies were finally included. The literature search and screening process are shown in Figure 1.

Figure 1. Flowchart of document retrieval. CNKI = China National Knowledge Network, WOS = Web of Science.

2.2. Statistical analysis

CiteSpace 6.3.R1 software and VOSviewer 1.6.19 software were used for bibliometric analysis of 4460 Chinese and 2372 foreign literatures, respectively. Cluster analysis, visualization, knowledge graph analysis, and corresponding visual maps were carried out from the aspects of author, institution, co-cited references, and keywords by VOSviewer.[19] Emergent words and time line evolution of the included literature analysis were made by CiteSpace. Microsoft Office 365 Excel can draw a table to show annual publication trends. SCImago Graphica was used to visualize the national distribution of the publications.[20]

2.2.1. Set VOSviewer software parameters

The CNKI literature data were imported into VOSviewer in Endnote format and WOS literature data in plain text. The minimum occurrence frequency of keywords in CNKI and WOS literature is set to 9. The minimum number of files of authors, institutions or co-cited references is set to 9. Then select both Network Visualization and Density Visualization for visualization analysis.[21,22]

2.2.2. Set CiteSpace software parameters

In the Time Slicing module, the year 2004–2023 is selected as the time span area, and the time partition is 1 year. For the Term Source module, “Title,” “Abstract,” “Author,” and “Keywords” are selected as the etymology, respectively. Select “Keyword” for the Node Types module. Select “Cosine” for Links, “Within Slices” for Scope, and set “Top N” to 50 for Selection Criteria.[23–25] Under Pruning module, choose “Pathfinder” to build algorithm and “Pruning sliced networks” to cut strategy.[26,27] The above literature data were analyzed by keyword timeline and emergent words.

3. Results

3.1. Annual publication volume analysis

The trend of publication can reflect the overall development of the research field.[28] The target literature of CNKI and WOS was statistically analyzed, and the annual published literature trend is shown in Figure 2.

Figure 2. Trend chart of annual published literature. CNKI = China National Knowledge Network, WOS = Web of Science.

For nearly 20 years, WOS database HFRS Overall, the change trend of the number of published documents showed a positive growth trend. However, the number of published documents on HFRS in CNKI generally showed a downward trend with the annual change trend. Before 2019, with the exception of 2016, the number of articles published by CNKI was significantly higher than the WOS database. After 2019, WOS included more documents than CNKI, they are 2019 (161 VS 137), 2020 (128 VS 125), 2021 (142 VS 114), 2022 (129 VS 86), and 2023 (133 VS 90). HFRS research at WOS peaked in 2015 with 183 publications. The number of studies from 2004 to 2008 was small, showing a rising trend. In the period from 2009 to 2015, the number of published papers increased linearly, and the number of published papers every year was more than 100, and HFRS research in this stage became popular. The period from 2016 to 2023 fluctuates and shows an overall growth trend.

The number of HFRS issued by CNKI can be roughly divided into 3 stages. In the first stage, the number of papers published from 2004 to 2008 was more than 300 each year. The year 2005 was a peak for HFRS-related research, with 426 publications, the highest number of publications in the past 20 years. In the second stage, the number of published papers decreased rapidly from 2009 to 2015, but the number of published papers each year was more than 200. From 2016 to 2023, the number of published papers continued to decrease, and the number of published papers in 2023 was 90.

3.2. Analysis of the cooperation networks of authors, institutions, and countries

3.2.1. Author cooperation graph analysis

Through VOSviewer software, the authors cooperation network of CNKI and WOS literature in the field of HFRS research is drawn, respectively. In the visualization map, each node represented a different author.[29,30] The size of the node represented the number of papers published by the author, and the thickness of the lines signified the intensity of collaboration between the authors. The authors in different clusters were represented by different colors, representing different research teams.[31]

Between 2004 and 2023, 4460 HFRS-related articles from CNKI were published by 8851 scholars. The top 3 authors in CNKI literature research are Bai Xuefan (33 articles), Wang Zhiqiang (33 articles), and Li Qi (31 articles), as shown in Figure 3. It should be noted that the top 3 authors were in different teams, respectively. 12,538 scholars contributed to 2372 HFRS-related articles from WOS. The top 3 authors in the literature research of WOS are Vaheri, Antti (53 articles), Ahlm, Clas (40 articles), Zhang, Yun (35 articles) and Song, Jin-won (35 articles), as shown in Figure 4. The 4 authors were also in different teams. Table 1 displays the top 10 authors based on the publication number via Vosviewer.

Table 1 The top 10 authors in CNKI and WOS.

Rank	CNKI	Rank	WOS	
Author	Publications (n)	Author	Publications (n)	
1	Bai Xuefan	33	1	Vaheri, Antti	53	
2	Wang Zhiqiang	33	2	Ahlm, Clas	40	
3	Li Qi	31	3	Zhang, Yun	35	
4	Han Zhanying	29	4	Song, Jin-won	35	
5	Zhang Yanbo	28	5	Mustonen, Jukka	33	
6	Wang Pingzhong	26	6	Jin, Boquan	29	
7	Qi Shunxiang	24	7	Zhang, Yusi	29	
8	Wei Yamei	23	8	Makela, Satu	29	
9	Du Hong	23	9	Krueger, Detlev H.	29	
10	Xu Yonggang	22	10	Ma, Ying	28	
CNKI = China National Knowledge Network, WOS = Web of Science.

Figure 3. Spectrum of high-frequency author cooperation network in CNKI literature. Each node represents a different author. The size of the node represents the number of papers published by the author, and the thickness of the lines signifies the intensity of collaboration between the authors. The authors in different clusters are represented by different colors, representing different research teams. CNKI = China National Knowledge Network.

Figure 4. Spectrum of high-frequency author cooperation network in Web of Science literature. Each node represents a different author. The size of the node represents the number of papers published by the author, and the thickness of the lines signifies the intensity of collaboration between the authors. The authors in different clusters are represented by different colors, representing different research teams.

3.2.2. Institution cooperation graph analysis

In the analysis of institutional cooperation network, each node represented a different institution.[32] The size of the node represents the number of documents issued by an institution, and the thickness of the lines represents the number of co-occurrences between each institution and other institutions, which reflects the cooperation and communication relationship between institutions to a certain extent. The institutions in different clusters were represented by different colors, representing different research teams. The more publications an institution has, the greater its contribution to the field of HFRS research.[33,34]

There are 3856 and 3427 research institutions from CNKI and WOS for literature visualization analysis, respectively. The top 10 institutions in the CNKI and WOS databases with the number of HFRS publications are mainly from universities, Centers for Disease Control and Prevention, hospitals, and research institutes, and the distribution of institutions is shown in Table 2. The top 3 institutions in CNKI literature research are Zhejiang Center for Disease Control and Prevention (28 articles), Liaoning Provincial Center for Disease Control and Prevention (25 articles), and Shandong University (24 articles), as shown in Figure 5. However, the 3 institutions are located in different cooperative clusters. The top 3 institutions in the literature research of WOS are Univ Helsinki (102 articles), Minist Hlth (71 articles), and Centers for Disease Control and Prevention (70 articles), as shown in Figure 6. Minist Hlth and Centers for Disease Control and Prevention are in the same cluster, while Univ Helsinki is in another cluster.

Table 2 The top 10 institutions in CNKI and WOS.

Rank	CNKI	Rank	WOS	
Institution	Publications (n)	Institution	Publications (n)	
1	Zhejiang Provincial Center for Disease Control and Prevention	28	1	Univ Helsinki	102	
2	Liaoning Provincial Center for Disease Control and Prevention	25	2	Minist Hlth	71	
3	Shandong University	24	3	Centers for Disease Control and Prevention	70	
4	Shandong Provincial Center for Disease Control and Prevention	23	4	Umea Univ	52	
5	Xi ‘an Eighth Hospital	22	5	Inst Pasteur	50	
6	Guangzhou Center for Disease Control and Prevention	20	6	Chinese Centers for Disease Control and Prevention	49	
7	Hubei Provincial Center for Disease Control and Prevention	20	7	Karolinska Inst	49	
8	Yunnan Provincial Institute of Endemic Disease Prevention and Control	18	8	Tampere Univ Hosp	48	
9	Shenyang Sixth People’s Hospital	18	9	Korea Univ	45	
10	Hebei Provincial Center for Disease Control and Prevention of viral diseases	18	10	Hokkaido Univ	41	
CNKI = China National Knowledge Network, Hosp = Hospital, Inst = Institute, Minist Hlth = Ministry of Health, Univ = University, WOS = Web of Science.

Figure 5. The institutions networks of studies from CNKI. Each node represents a different institution. The size of the node represents the number of documents issued by an institution, and the thickness of the lines represents the number of co-occurrences between each institution and other institutions. CNKI = China National Knowledge Network.

Figure 6. The institutions networks of studies from the Web of Science. Each node represents a different institution. The size of the node represents the number of documents issued by an institution, and the thickness of the lines represents the number of co-occurrences between each institution and other institutions.

3.2.3. Country cooperation graph analysis

SCImago Graphica was used to analyze the number of national publications and the spatial co-occurrence of 2372 literature from WOS. In the graph, each node represented a different country. The size of the node represented the number of papers published by the country, and the thickness of the lines signified the intensity of collaboration between the countries.[35] A total of 144 countries around the world had conducted studies on the association of HFRS. Figure 7 shows spatial distribution of the publications and these top countries were almost in North America, Asia, Europe, and South America. Among them, the top 3 countries in the literature research of WOS are the USA (669 articles), China (464 articles), and Germany (195 articles), as shown in Figure 8.

Figure 7. Geographical distribution of national publications. Each node represents a different country. The size of the node represents the number of papers published by the country, and the thickness of the lines signifies the intensity of collaboration between the countries.

Figure 8. The distribution of the top 10 countries that had the most publications.

3.3. Co-cited references graph analysis

References represent the basic knowledge of a specific research field. Analysis of the 2372 literature from WOS indicated there were 49,165 references, with an average of 20 references per publication. From 2004 to 2023, the top 10 HFRS-related papers with the highest citation counts are shown in Table 3. Jonsson Cb was the author of the first total cited paper. In 2010, the author published the article titled “A global perspective on hantavirus ecology, epidemiology, and disease,” which has now received 265 citations. Schmal John C’s paper from the journal Emerging Infectious Diseases ranked second with 237 citations overall, and Vapalahti O’s article “Hantavirus infections in Europe” ranked third with 198 citations. Of all the references cited, 205 were cited more than 30 times. We used VOSviewer to perform cluster analysis on 205 pieces of literature that have been cited more than 30 times. Figure 9 showed that the cited literature was divided into 5 clusters (each represented by a different color), representing 5 major research areas.

Table 3 The top 10 cited references on HFRS from Web of Science.

Rank	Title	Journal	First author	The year of publication	Citations (n)	
1	A global perspective on hantavirus ecology, epidemiology, and disease	Clinical Microbiology Reviews	Jonsson Cb	2010	265	
2	Hantaviruses: A global disease problem	Emerging Infectious Diseases	Schmal John C	1997	237	
3	Hantavirus infections in Europe	Lancet Infectious Diseases	Vapalahti O	2003	198	
4	Uncovering the mysteries of hantavirus infections	Nature reviews microbiology	Vaheri A	2013	163	
5	Dengue and dengue hemorrhagic fever	Clinical Microbiology Reviews	Gubler Dj	1998	150	
6	Isolation of the etiologic agent of Korean hemorrhagic fever	The Journal of Infectious Diseases	Lee Hw	1978	147	
7	The global distribution and burden of dengue	Nature	Bhatt S	2013	133	
8	Hantavirus Infections in Humans and Animals, China	Emerging Infectious Diseases	Zhang Yz	2010	116	
9	Rapid detection and typing of dengue virus from clinical-samples by using reverse transcriptase-polymerase chain-reaction	Journal of Clinical Microbiology	Lanciotti Rs	1992	109	
10	Hantavirus pulmonary syndrome - pathogenesis of an emerging infectious disease	American Journal of Pathology	Zaki Sr	1995	100	
HFRS = hemorrhagic fever with renal syndrome.

Figure 9. Network map of co-citation references of publications on HFRS that were cited at least 30 times. Each node represents a different reference. The size of the node represents the number of references cited, and the thickness of the lines signifies the intensity of collaboration between the references. HFRS = hemorrhagic fever with renal syndrome.

3.4. Research hotspots analysis

3.4.1. Keywords co-occurrence and cluster analysis

Paper keywords are the author’s condensed results of the full text, which can reflect the overall research content of the paper, and the analysis of keywords can reveal the core research content of a certain field to a certain extent.[36] According to the parameters set above, this research selects 80 CNKI literature keywords and 434 WOS literature keywords through VOSviewer software, and draws visual pictures of them respectively. In the VOSviewer keyword co-occurrence network graph, each node represented a different keyword. The same color represents a unified cluster, and the size of nodes in the graph is proportional to the number of posts.[37]

As can be seen from Figure 10, keywords in CNKI literature are mainly divided into 6 clusters, which are represented by 6 colors: red, green, blue, yellow, purple, and cyan. Red clusters consisted of epidemic, nursing, hemodialysis, treatment, complications, clinical analysis. Green clusters consisted of epidemic hemorrhagic fever, hantavirus, disease control, plague, rodents. Blue clusters were composed of renal failure, host animal, epidemic trend, epidemiological investigation, rat density. Yellow clusters were composed of epidemic characteristics, incidence, infectious diseases, prevention and control, meteorological factors, monitoring. Purple clusters were composed of HFRS, epidemic analysis, influencing factors, risk factors, control. The keywords of cyan clusters were model, time series, arima model, prediction, gm model.

Figure 10. CNKI keyword co-occurrence network diagram. Each node represents a different keyword. The same color represents a unified cluster, and the size of nodes in the graph is proportional to the number of posts. CNKI = China National Knowledge Network.

As shown in Figure 11, keywords in WOS literature are also divided into 6 clusters: red, green, blue, yellow, purple, and cyan. Red clusters consisted of virus, dengue, surveillance, dynamics, aedes-aegypti. Green clusters consisted of renal syndrome, hantaan virus, hantavirus pulmonary syndrome, vaccine, identification. Blue clusters were composed of epidemic, infection, outbreak, diagnosis, antibody. Yellow clusters were composed of hantavirus, puumala virus diversity, rodents. Purple clusters were composed of hemorrhagic fever, pathogenesis, emergence. The keywords of cyan clusters were disease, virus-infection, risk factors. The top 30 high-frequency keywords from CNKI and WOS are shown in Table 4.

Table 4 The top 30 high-frequency keywords from CNKI and WOS.

Rank	CNKI	Rank	WOS	
Keywords	Count (n)	Keywords	Count (n)	
1	Hemorrhagic fever with renal syndrome	1772	1	Hemorrhagic fever	1195	
2	Epidemic hemorrhagic fever	731	2	Renal syndrome	443	
3	Hantavirus	276	3	Hantavirus	428	
4	Epidemic characteristics	234	4	Infection	366	
5	Monitor	228	5	Epidemic	354	
6	Nurse	167	6	Virus	315	
7	Epidemiology	142	7	Disease	288	
8	Analyze	131	8	Outbreak	245	
9	Hemorrhagic fever	128	9	Transmission	222	
10	Misdiagnose	126	10	Hemorrhagic fever with renal syndrome	213	
11	Incidence rate	102	11	Dengue	200	
12	Hemodialysis	95	12	Hantaan virus	180	
13	Epidemic	95	13	Nephropathia-epidemica	178	
14	Host animal	91	14	Epidemiology	174	
15	Rat density	85	15	Pathogenesis	168	
16	Infectious disease	75	16	Diagnosis	133	
17	Forecast	59	17	Puumala virus	126	
18	Heal	59	18	Virus-infection	119	
19	Acute renal failure	59	19	Infections	116	
20	Prognosis	46	20	Pulmonary syndrome	111	
21	Complication	46	21	Hantavirus infection	106	
22	Epidemiological characteristics	41	22	Risk factors	102	
23	HFRS	41	23	Antibody	100	
24	Diagnosis	40	24	Evolution	95	
25	Clinical features	39	25	Hantavirus pulmonary syndrome	94	
26	Fashion trend	39	26	HFRS	86	
27	Hemorrhagic fever with renal syndrome/diagnosis	37	27	Children	84	
28	Vaccine	36	28	Dynamics	83	
29	Renal syndrome	33	29	Ebola	78	
30	Hemorrhagic fever with nephrotic syndrome	32	30	Identification	73	
CNKI = China National Knowledge Network, HFRS = hemorrhagic fever with renal syndrome, WOS = Web of Science.

Figure 11. Web of Science keyword co-occurrence network diagram. Each node represents a different keyword. The same color represents a unified cluster, and the size of nodes in the graph is proportional to the number of posts.

The density visualization map is drawn based on the VOSviewer keyword co-occurrence network diagram, as shown in Figures 12 and 13. The closer the color of keyword nodes is to red, the higher their co-occurrence frequency, which further reflects the hot spots in this research field.

Figure 12. Visualization of CNKI keyword density. The closer the color of keyword nodes is to red, the higher their co-occurrence frequency, which further reflects the hot spots in this research field. CNKI = China National Knowledge Network.

Figure 13. Keyword density visualization of Web of Science. The closer the color of keyword nodes is to red, the higher their co-occurrence frequency, which further reflects the hot spots in this research field. PCR = polymerase chain reaction, RT-PCR = reverse transcription-polymerase chain reaction.

3.4.2. Keywords timeline analysis

The timeline spectrum can directly reflect the hot spots in different research fields and their derivative relationships in different time periods, and then predict the future development trend.[38] Citespace was used to establish a clustering timeline of HFRS literature from 2004 to 2023. Figures 13 and 14 show the timelines of keyword co-occurrence of literature from CNKI and WOS. In the time diagram, the node size is proportional to the frequency of keyword occurrence, and the color depth is related to the occurrence time. The color gradient legend is shown in the lower left corner.[39]

Figure 14. Timeline graph of HFRS research based on CNKI. The node size is proportional to the frequency of keyword occurrence, and the color depth is related to the occurrence time. The color gradient legend is shown in the lower left corner. CNKI = China National Knowledge Network, HFRS = hemorrhagic fever with renal syndrome.

As Figure 14 shows, CNKI literature studies on HFRS extracted a total of 9 cluster labels, which represent the subject content of HFRS research in the past 20 years. These labels were nursing, hantavirus, epidemic characteristics, surveillance, vaccine, clinical characteristics, injury, platelets, and ribavirin. As Figure 15 shows, WOS literature studies on HFRS extracted a total of 7 cluster labels: hantavirus, dengue, aedes-aegypti, pulmonary syndrome, ebola virus disease, Saudi Arabia, community particative.

Figure 15. Timeline graph of HFRS research based on Web of Science. The node size is proportional to the frequency of keyword occurrence, and the color depth is related to the occurrence time. The color gradient legend is shown in the lower left corner. HFRS = hemorrhagic fever with renal syndrome.

3.4.3. High emergence keyword analysis

Keyword co-occurrence[40] can concentrate on the problem or concept studied and provide a scientific summary of the research hotspot, while keyword emergence further represents the frontier of the research field and can deeply explore the emerging trend in the field.[41–43] Citespace was used to analyze the emergent words from CNKI or WOS literature, and the top 20 phrases are listed in Figures 16 and 17, respectively. The outburst word map includes outburst keywords, outburst strength, start, and end time, etc. The map was sorted according to the start time of outburst words in HFRS. In the figure, the red line segment represented the period in which keywords appeared, and the positions at both ends corresponded to the begin and end year. The green line segment represented the years except the period in which the keywords appeared.[21,44] Summarizing the top 20 phrases of emergent intensity can help us to obtain the main hot spots in the field of HFRS in the past 20 years.

Figure 16. Keyword emergence of HFRS literature based on CNKI database. The red line segment represents the period in which keywords appear, and the positions at both ends corresponded to the begin and end year. The green line segment represents the years except the period in which the keywords appeared. CNKI = China National Knowledge Network, HFRS = hemorrhagic fever with renal syndrome.

Figure 17. Keyword emergence of HFRS literature based on Web of Science database. The red line segment represents the period in which keywords appear, and the positions at both ends corresponded to the begin and end year. The green line segment represents the years except the period in which the keywords appeared. HFRS = hemorrhagic fever with renal syndrome.

The top 20 keywords with the strongest occurrence burst in CNKI literature are shown in Figure 16. Keywords “misdiagnose” with the strongest citation bursts (10.28) appeared in 2005, followed by “epidemic characteristics” (8.99), “prognosis” (7.89). According to the results of the emergence analysis, we can roughly divide the development process of HFRS research in CNKI database into 3 stages. The first phase spanned roughly 2004–2011. The research focus is on the vaccine and treatment of HFRS, and the main breakout words are “antibody” (4.46, 2004–2008) and “misdiagnosis” (10.28, 2005–2011). The period from 2009 to 2015 is the second stage. The research focus turns to nursing and surveillance, and the main breakout words are “nursing” (7.83, 2009–2013) and “epidemic analysis” (3.79, 2011–2016). 2016–2023 is the third stage, the research trend turns to the prognosis and prediction of HFRS. The main emerging words are “quality care” (7.29, 2016–2021), “epidemic surveillance” (2.43, 2016–2020), and “meteorological factors” (3.16, 2016–2023).

The top 20 keywords with strongest citation bursts in WOS are presented in Figure 17. Keywords “ebola virus disease” with the strongest citation bursts (11.7) appeared in 2015. The research and development process of HFRS in the WOS database shifted from “monoclonal antibody” (6.94) and “linked immunosorbent assay” (6.41) to viral experimental studies, which mainly reflected in “puumala hantavirus” (5.79) and “thottapalayam virus” (5.59). After 2014, the research focused on “epidemics” (5.26), “ebola virus disease” (11.7), and “association” (3.73).

4. Discussion

We collected 4460 Chinese academic studies assembled by CNKI and 2372 English research articles collected by WOS from 2004 to 2023 as research objects. We conducted scientometric analysis to identify the basic situation, cooperation networks, hotspots, and research frontiers of HFRS research, and our discussions are as follows.

4.1. Basic situation

At present, more than 30 countries in the world have reported the outbreak of HFRS, and China is the most seriously affected country.[45,46] According to the analysis of the number of published articles, the overall decline trend of the number of Chinese articles on HFRS can be seen, indicating that the epidemic has been gradually controlled in China through measures such as vaccination, rodent control, treatment, and nursing,[1] and the incidence has gradually stabilized since 2012. The article in the WOS data bank surpassed the number of domestic studies after 2019, indicating that the HFRS epidemic is still prevalent in the world, and scholars in many countries are concentrating their efforts to solve the problems in this field.

It can be found from the collaborative visualization map of authors or institutions that many authors and institutions are involved in the research of HFRS, forming different cooperative clusters. In the same cooperative cluster, there is close cooperation between authors or institutions, but in different cooperative clusters, there is much less cooperation between authors or institutions. This suggests that while many authors and institutions are interested in the study of HFRS, there is limited collaboration and contact between authors or institutions in different collaborative clusters. Therefore, strengthening collaboration between authors or institutions across different clusters can improve the quantity and quality of HFRS literature research.

From the analysis results of the number of published papers and co-occurrence by countries, we found that the United States is the country with the largest number of published papers, followed by China, Germany, England, and France, indicating that these countries are the leaders of current HFRS research, and at the same time, these countries are also the main prevalence areas of HFRS.[38] Among the 2372 articles analyzed from WOS database, 1328 were from the United States, China, and Germany, indicating that these 3 countries have the most academic exchanges and cooperation and the greatest international influence.

The cluster graph of co-cited literature generated by VOSviewer shows 5 cluster groups representing five basic research fields: the structure and function of hantavirus; identification of hantavirus; clinical symptoms and pathophysiology of HFRS; ecological and epidemiological studies on HFRS; and HFRS-related vaccine research. In the early investigation, “HFRS cases” and “renal syndrome” were the main topics. However, current HFRS research focuses on “meteorological factors” and “vaccines.” This finding indicates that early HFRS research focused on the onset of symptoms and treatment methods. The focus was then shifted to meteorological factors and vaccine research and development to determine the causes of HFRS and devise preventive measures.

4.2. Hotspots and research frontiers of HFRS research

According to the list of top 30 keywords occurrence frequency, the important research fields at home and abroad are mainly focused on the symptoms, infection, nursing, virus epidemiology, and epidemic surveillance of HFRS. Using VOSviewer to cluster and density analysis of keywords, the map shows that there is superposition between clusters, indicating that each cluster is different, but interrelated. These keywords from CNKI or WOS were divided into 6 clusters, suggesting 6 research fields. The 6 research areas of CNKI mainly focused on clinical complications, disease control, epidemic trend, meteorological factors, epidemic analysis, and prediction model. The 6 research areas of WOS mainly focus on virus research, clinical symptoms, vaccine research, host animals, pathogenesis, and influencing factors.

Combined with the emergence analysis of keywords and the analysis of the evolution of the time line, it was found that as a zoonotic disease, Hantavirus mainly transmitted HFRS through mice as a vector, and the viruses carried by the mice also spread with the change of survival and activity range. Therefore, many Centers for Disease Control and Prevention have included Apodemus agrarius, the host animal for monitoring HFRS, as an important part of their work. In addition, due to climate change and the acceleration of urbanization,[4] along with changes in temperature, rainfall, land use type, population density, agricultural productivity, and other factors, hantavirus-infected rats are distributed in different areas of the city, and contact with human bodies also leads to the epidemic and spread. This is also the emerging hot spot of HFRS prevention and control work and research.

The outbreak of hemorrhagic fever is a serious public health event, and its outbreak and spread pose a serious threat to people’s health and life safety. In recent years, with the in-depth study of hemorrhagic fever epidemic by domestic and foreign scholars, more and more teams began to use the method of time series analysis to predict the epidemic. Time series forecasting is a statistical method to predict future trends based on historical data. Through time series prediction, we can better understand the transmission characteristics and development trend of hemorrhagic fever outbreaks. The prediction results can help us identify high-risk areas and populations where the epidemic may break out in advance, so as to strengthen surveillance and prevention and control measures. However, because the spread of the hemorrhagic fever epidemic is affected by many factors, such as climate, environment, population density, living habits, etc, there may be some errors in the prediction results. Therefore, scholars have comprehensively considered a variety of factors to continuously improve and optimize the prediction model. According to the analysis results of The Boosted Regression Tree (BRT),[4] Population density (15.90% relative contribution), altitude (12.02%), grassland (11.06%), cultivated land (9.98%), rural residential area (9.25%), forest land (8.71%), and water body (8.63%) were relatively important factors affecting the prevalence of HFRS.

4.3. The future research direction

In order to improve the accuracy of the prediction model, new models have been constantly applied to the prediction of HFRS, such as the Bayesian space-time analysis method[47] and geographical weighted regression model.[48] The results show that the spatial distribution of HFRS has obvious positive spatial correlation. Therefore, when exploring the future research direction, we can think about the field of HFRS from multiple dimensions, such as developing more accurate prediction models, exploring new prevention and control strategies, and deepening disease mechanism research. Comprehensive multi-factor prediction model: Future studies can further integrate more clinical information, genetic polymorphisms, biomarkers, and other data to build a more comprehensive and accurate prediction model; using machine learning algorithms such as random forests, gradient lift trees (gradient boosting decision trees), or deep learning techniques to train large amounts of data to improve the prediction accuracy and generalization ability of models. Exploring new prevention and control strategies: further promote the research and development of HFRS vaccines, especially those against novel virus strains. At the same time, the existing vaccine vaccination strategy should be optimized to improve the vaccination coverage and immunization effect, and reduce the incidence and severe disease rate of HFRS; Strengthen environmental interventions to reduce rodent habitats and transmission routes. Deepening disease mechanism research: to study the interaction mechanism between HFRS virus and host cells, and reveal the key links in the process of virus invasion, replication, and disease. To explore the characteristics and regulatory mechanisms of immune response in patients with HFRS, and to understand the role and changes of immune cells in disease progression.

To sum up, future research should focus on developing more accurate prediction models, exploring new prevention and control strategies and deepening disease mechanism research, so as to make greater breakthroughs and progress in the prevention and treatment of HFRS.

5. Limitation

The limitations of the study should not be ignored. Firstly, in order to avoid differences in literature collection, this study only included the literature retrieved from WOS core collection and did not search the literature collected in other databases, which inevitably resulted in literature omission. Secondly, because citations may be affected by various factors, such as academic environment, academic norms, academic evaluation mechanism, etc, bibliometrics cannot accurately reflect the actual quality and influence of academic papers.

6. Conclusion

In this study, CiteSpace and VOSviewer were used for the first time to visually analyze the literature retrieved from the HFRS-related research fields in CNKI and WOS databases in the past 20 years. We found that in the past 20 years, the number of published English literature related to HFRS has been on the rise, and the international community has gradually attached importance to relevant research in this field. The United States, China, Germany, the United Kingdom, and France are all countries that attach great importance to HFRS research, and the top research institutions and lead authors are also located in these countries. Collaboration between authors or institutions in different collaborative clusters should be further strengthened to improve the quantity and quality of HFRS literature research. The analysis results of hot spots and trends suggest that we need to develop more reliable tools and methods in the monitoring and spatio-temporal analysis of HFRS epidemic data in the future, so as to provide references for the surveillance and early warning of zoonotic diseases in the field of public health research.

Acknowledgments

We thank the Jiangxi Young Scientific Research and Innovation Team for their great support and help.

Author contributions

Conceptualization: Wenfang Zhou.

Data curation: Wenfang Zhou, Hongyu Si.

Formal analysis: Wenfang Zhou, Yonghai Dong, Xiaoqing Liu, Hongyu Si, Cheng Yang.

Software: Wenfang Zhou, Xiaoqing Liu, Sheng Ding.

Writing – original draft: Wenfang Zhou.

Funding acquisition: Yonghai Dong.

Investigation: Yonghai Dong, Cheng Yang.

Supervision: Yonghai Dong, Sheng Ding, Hongyu Si.

Writing – review & editing: Yonghai Dong, Sheng Ding.

Resources: Xiaoqing Liu.

Visualization: Cheng Yang.

Abbreviations:

BRT Boosted Regression Tree

CNKI China National Knowledge Network

GBDT Gradient Boosting Decision Trees

HFRS hemorrhagic fever with renal syndrome

HV Hantavirus

Hosp Hospital

Minist Hlth Ministry of Health

prc polymerase chain reaction

rt-prc reverse transcription-polymerase chain reaction

Univ University

WOS Web of Science

This work was supported by the National Natural Science Foundation of China (Grant No. 82360661), Jiangxi Provincial Key Laboratory of Major Epidemic Prevention and Control (Grant No. 2024SSY06021), the Leading Medical Discipline (Epidemiology) of Jiangxi Province, China, and Jiangxi Provincial Center for Disease Control and Prevention Science and Technology Innovation “Pei Ying” Program (PYJH202405).

The research included data from CNKI database and Web of Science (WOS) core collection database; no human subjects, human data, tissue, or animals were used.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Zhou W, Dong Y, Liu X, Ding S, Si H, Yang C. A bibliometric analysis of domestic and international research on hemorrhagic fever with renal syndrome over the past 2 decades. Medicine 2024;103:37(e39737).
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