Inuenza Global Scholarly Publications in the Mirror of the Co-word Analysis Technique

Background and Aim: Inuenza is a global epidemic disease that always causes irreparable damage to individuals and countries' health, and the amount of research in this area is increasing. The primary purpose is to discover the hidden patterns and emerging events of global Inuenza publications. Methods: This article is an applied type that has been done with an analytical approach and using the Co-word analysis method. This research's statistical population includes all the keywords of documents indexed with inuenza's subject in the Web of Science Core Collection (WOSCC) in the last two decades (2000-2019). The number of keywords studied in this article is 26767. MeSH was used to determine the search strategy for document retrieval. SPSS and UCINET software were used for data analysis, and VOSviewer was utilized for drawing the map. Results: The keywords "Avian Inuenza" and "Inuenza Vaccine" with a frequency of 1302 and 1264 were the most frequent keywords in the subject area of inuenza. Findings related to the analysis of clusters by the hierarchical clustering method led to the creation of 21 clusters in the eld of inuenza. Seventeen clusters with the most keywords were the largest cluster. The strategic diagram indicated that most inuenza subject area clusters are located in the third quadrant of the strategic diagram and are considered emerging or declining subjects. communities, research allocation of facilities budget.

(swine) (9), and humans have been introduced as a constant threat to human beings (2). In uenza and its complications and consequences lead to many deaths worldwide every year (10). The largest and deadliest pandemic of in uenza in the twentieth century was Spanish in uenza in 1918 (11).
Approximately 500 million people were infected, and between 50 and 100 million people died, more than the number of victims who were directly participated in World War I. From 1918 until about two decades ago, on average, a global in uenza epidemic has occurred every 10-15 years (11, 12, 13, and 14).
There are several methods to evaluate scienti c publications. Scientometrics is one of the most common approaches for assessing scienti c activities and managing the studies (15). As one of the essential techniques of scientometrics, the co-word analysis makes it possible to analyze the content of texts and scienti c documents of research areas (16,17), and thus the main concepts of scienti c areas can be identi ed. By this knowledge, the patterns, networks, and conceptual events of the scienti c areas can be visualized, the hierarchical relationships of the conceptual issues can be discovered, and also a piece of research can be carried out to manage the speci c area (18). Co-word analysis also measures and analyzes the degree of relevance between topics, concepts, and terminology of ideas related to a topical area (19) and provides researchers with a powerful tool for knowledge discovery, drawing bibliographic maps, and drawing conceptual maps. The analysis of scientometric indicators from different scienti c areas can be a useful and e cient tool for the management and policy-making of science research (20).
In uenza, especially highly pathogenic in uenza, a zoonotic disease in seasonal epidemics or pandemics, spreads rapidly among susceptible people, threatens public health, and imposes a signi cant economic burden on countries. Unique research appears to be essential to discover the hidden patterns and emerging events of international in uenza publications. The present study's objective is to apply scientometric techniques and the co-word analysis method to evaluate the publication trend of in uenza topical area and draw the scienti c structure map of the mentioned topical area.
In the following, an important and relevant literature review on co-word analyzes has been presented.
Since this research is in medical sciences, only some of the studies conducted in the topical medical sciences have been reviewed, performed by co-word analysis, and published at national and international levels.
Makkizadeh and Bigdeloo (21) have studied the intellectual structure of knowledge in andrology using coword analysis. The results indicate that the United States, China, Italy, and Iran are the countries with the most signi cant number of publications in this eld. "Spermatozoa" is the most important word in this research. Hierarchical clustering results led to the introduction of 13 clusters. Wei et al. (22) also investigated the topical area of "Disaster Medicine" by co-word analysis. High-frequency keywords were classi ed into 6 clusters. The medical rescue had the highest degree and betweenness in terms of centrality indicators, among other keywords. Afterward, Makkizadeh (23) studied the intellectual structure of knowledge in nanomedicine by the co-word method. This article evaluated the documents indexed in PubMed for ten years. The results indicate that "nanoparticles" is the most frequent keyword in this topical area. Moreover, the results of hierarchical clustering analysis indicate the existence of nine clusters. "Drug Delivery System (emphasis on cancer)" is the main cluster of this study. In another study, Batcha (24) studied international swine in uenza research focusing on Indian scienti c publications over 25 years. The publication trend of scienti c studies on swine in uenza in India was incremental, and the most signi cant international scholarly collaboration was between India and the USA. In line with the previous study, Fricke et al. (25) investigated the topical in uenza area with 51418 documents published by 151 countries. The results indicated that more than 37% of total publications in this eld had been published by American, British, and German researchers. In a scientometric study, Selvaraj (26) investigated the topical area of the avian in uenza virus over 55 years and concluded that the average publication per year is equal to 207 documents. The highest number of articles (1084 articles) was published in 2010; China and the USA, with 1618 and 738 articles, have published the highest number of publications in the studied topical area. In line with previous research, Preeti and Rishabh (27) Rathinasabapathy and Kopperundevi (28) presented the international scienti c articles on avian in uenza virus at a conference. The results of their investigation showed that 5,209 documents were published in the topical area of avian in uenza between 1971 and 2014. The results indicated that 4632 articles among the total number of published documents were published after 2000. In another article, Danesh & Ghavidel (29), by the visualization of the fourteen clusters of the topical area of HPV, discovered that most papillomavirus's topical clusters were less developed little interest in the global research network, and also possessed low density and centrality. Furthermore, Faizabadi & Vaziri (30) investigated the studies on dementia by using word co-occurrence analysis over ve years. The results show the upward trend of scienti c publications in this topical area. In another article, Khasseh et al. (31) by evaluating the scientometrics of scienti c publications of Iranian researchers in the eld of parasitology, indicated that from 1980 to 2009, the number of 392 articles out of the total number of published documents with organizational a liation in Iran were related to parasitology; the researchers of Tehran University of Medical Sciences have published the most scienti c documents in this topical area.
The literature review showed that many researchers had applied scientometric methods, especially coword analysis, to study the intellectual and conceptual structure in various topical areas of medical and paramedical sciences. Furthermore, this methodology provided a lucid and logical view of various topical areas of medical sciences. In recent years, researchers' interest in co-word analysis to review international scienti c publications has increased, and co-word analysis was useful in mapping the structure of various scienti c areas, especially medical sciences. Researchers have applied co-word analysis to study various types of in uenza virus in different years. However, no comprehensive and up-to-date research was carried out in the topical in uenza area over the last twenty years.
Due to the extent of scienti c publications on in uenza and the high importance of this issue, and the lack of up-to-date and comprehensive scientometric studies by co-word analysis, the necessity for conducting the present research is becoming more apparent. Since a large part of the world's population is always infected with this virus, especially at certain times of the year (fall and winter), scientists have conducted extensive research on in uenza and published the results in the form of articles and scienti c reports. With the increasing trend of in uenza publications and the lack of up-to-date and comprehensive scientometric studies to evaluate this eld's concepts in the last two decades. This paper's ndings can be necessary and useful for in uenza researchers, policy-makers, and decisionmakers of the Ministry of Health and Medical science associations. The present investigation's main objective is to discover the hidden patterns and emerging events of international in uenza publications during the mentioned period, which has been conducted using the word co-occurrence analysis. Following the primary purpose, the present article seeks to answer the following questions: How is the distribution trend of the frequency of published documents? How is the frequency distribution of the keywords in the topical area of in uenza based on word co-occurrence? What are the topical areas and most extensively applied descriptors of in uenza worldwide? How is the clustering of in uenza-related topics performed based on cluster analysis?

Methods
The present study is a useful scienti c study conducted using the co-word analysis with an analytical approach. Co-word analysis is applied in a set of texts to identify connections between the items available in publications in that topical area (32). Moreover, co-word analysis is an objective and qualitative method based on the nature of words that involve essential ideas, knowledge, or scienti c concepts (15). The data required for the present research were extracted from the Web of Science Core Collection (WOSCC) on January 4, 2020. WOSCC was selected since it is older and more extensively used than other citation databases and provides more valuable cited data in various elds (33). A period of 20 years (2000 to 2019) was taken into account for data mining, which excellently showed knowledge's intellectual structure.
For precise evaluation of the research trends of in uenza topical area, the search keywords were selected through medical subject headings (Mesh: https://meshb.nlm.nih.gov). After consulting with experts, the search strategy with the keywords "In uenzas, Human" or "In uenza" or "Human Flu" or "Human In uenza" or "In uenza in Humans" or "In uenza in Human" or "Grippe" or "Flu Human" or "Human In uenzas" or "In uenzas" or "In uenza Pandemic" or "In uenza A" or "In uenza B" or "In uenza C" or "INFLUENZA A VIRUS" was performed with the TI tag and OR Boolean operator in the advanced search section and using the designed strategy of the search. The document type and language were limited to articles and English, respectively. After searching and retrieving documents, 26,840 unique keywords were extracted without duplication. Then, the obtained keywords were uni ed and re ned. The number of keywords was reduced to 26767. Ravar Matrix: Ravar PreMap software was applied for uni cation, development of the co-occurrence matrix, determining the threshold. For this purpose, after several trial and error steps, the "Bradford" rule was employed in the nal analysis so that the topics were initially arranged from high to low based on the frequency. The initial topics, the sum of frequencies of one-third of the total frequencies, were placed in the rst category: core topics. Moreover, the subject in the range of one-third to two-thirds of the total frequencies (middle one-third) in total frequency was selected as related to the core (34,35).
By placing inclusion thresholds on the keywords with frequencies higher than 56, the number of 96 keywords were placed in this range, which is capable of highlighting the main research content in the eld of in uenza (Table 1).
In the present study, co-word refers to the co-occurrence of keywords in international publications indexed in the WOSCC database. Co-word analysis was performed using hierarchical clustering methods, and cluster analysis was carried out using SPSS 16 software. This method highlights the primary and essential subjects and hidden connections of the in uenza topical area. Hierarchical clustering was conducted, and a dendrogram of the subjects was drawn using Ward and Squared Euclidean distance methods (Fig. 2). VOSviewer 1.6.13 was also applied to visualize the keywords.
Moreover, the co-word matrix network features, such as centrality and density, can be analyzed by UCINET 6.689 software in the form of a strategic diagram ( Fig. 3) to obtain more data about the co-word structure governing the studied topical area. If a node has many connections with other nodes in a network, it will have a higher value of centrality and an important place. Besides, higher density means more coherence and the internal correlation between nodes. The density of a topical area indicates its capacity to preserve and develop itself. The values of centrality and density of each cluster can be indicated in the form of a strategic diagram and reveal the status of the studied topical area's evolutionary tendencies. In a strategic diagram, the X-axis represents centrality, and the Y-axis is referred to as density. The strategic diagram can be shown in four quadrants, each of which has density and centrality, and each cluster in each quadrant of this diagram has a different situation (35). Visualization was performed using VOSviewer.

Results
The trend of Frequency Distribution of Documents in The

Ranking of Keywords and Topics in the In uenza Subject Area Based on Co-Word Analysis
After analyzing and unifying the keywords of the documents indexed in the WOSCC database from 2000 to 2019, 96 keywords in the in uenza topical area with a frequency higher than 56 were ranked based on the frequency; 15 keywords are listed in Table (1). Avian In uenza, In uenza Vaccine, and Vaccination with the frequency of 1302, 1264, and 1125, respectively, are placed in the rst to third ranks of top keywords in the in uenza topical area (Table 1). The core topics based on the most frequent keywords in the in uenza topical area, which involves 100 keywords with a frequency higher than 56, was drawn by VOSviewer in the form of a map and shown in Figure (1). In the map, the topics are clustered according to the available algorithms. The color of each item indicated the topics of that cluster. The color or diameter of the lines showed the relationship between the items. The topics with high frequency and co-occurrence are at the center of the gure, and surrounding topics have lower frequency and co-occurrence.
Map (1) indicated the network structure of high-frequency keywords in the in uenza topical area in a total of four clusters of different colors. In this map, each of the circles (nodes) represented a keyword. The lines are referred to as the connections between the nodes. The number of connections is greater than the number of nodes. The larger nodes are referred to the high-frequency keywords compared to other keywords and imply the core topics (Table 1). In MAP 1, high-frequency keywords such as "Avian In uenza" have larger circles in the center and are placed in the center, and keywords including "Seasonal In uenza Vaccine," "Pathology," "H5N8", "H5N2", and "Mathematical Model" included surrounding topics with lower co-occurrence (Table 1 and Map 1).
Clustering the Topics of International In uenza Publications Based on Cluster Analysis Using SPSS and calling co-occurrence matrices in this software, hierarchical clustering was performed, and a dendrogram of topics was drawn in this section of the research (Fig. 2).
Every single topic is considered as a branch. The analysis of the ndings related to co-word analysis had led to the formation of 21 topical clusters. Some keywords did not directly semantic connection with that cluster's subject in some clusters and the principal and essential keywords, which has been observed in co-word analysis studies. These keywords were less effective in terms of frequency and correlation coe cient than other keywords in that cluster (35). These keywords have attracted little attention from researchers (15). The keywords of the reviewed articles in twenty years have led to the formation of 21 clusters; the connection between them can understand the subject of that topical cluster ( Table 2). Cluster 1 included two keywords, "immunogenicity" and "safety," with the ranks 282 and 210, respectively, which raise the issue of safety control. In Cluster 2, the two keywords "LAIV" and "Clinical Trial" are referred to as the "Clinical trial of vaccines." Among the three Cluster 3, the keyword "In uenza Vaccination" is the most frequent. According to the keywords, it can be declared that the third cluster has been discussed regarding the vaccination of health care workers at risk of in uenza. Cluster 4 included ve keywords: the most frequent keyword of the cluster, "Vaccination," with a frequency of 1125, is the third place of most frequent keywords. According to the presence of the keywords "Immunization," "Prevention," "Vaccination," and also "FLU," it can be said that the subject of this cluster is about prevention and immunization by in uenza vaccine and according to the presence of the keyword "Pregnancy," this prevention and immunization can also be considered in interaction with pregnancy and pregnant mothers. Cluster 5 also possessed two keywords, "Biosecurity" and "Equine In uenza," which is referred to as the issue of biosecurity against in uenza (especially for animals). Cluster 6 had seven keywords; the keyword "hemagglutinin," as the fourth top keyword, is in this cluster. By a brief overview of this cluster's keywords, it can be said that the subject of this cluster is related to the "protective antibodies against in uenza proteins." The topics of Cluster 7 consisted of three keywords, and some topics such as "Interferon," "NS1", and "Innate Immunity" excellently indicate the innate immune of in uenza virus with interferon and NS1 protein. There are ve keywords in Cluster 8; the word "Cytokines" is the most frequent. According to the existed keywords in this cluster, the subjects of "Pathogenesis" and "Immunopathogenesis" of in uenza virus infection can be obtained from this cluster. Cluster 9 included three keywords, the "In uenza Vaccine," the second core keyword in the eld of in uenza, is among which. This keyword's presence connected with the other two keywords indicates the in uenza vaccine issue and its effects on the immune. Cluster 10 possessed two keywords, "In uenza-Like Illness" and "Respiratory viruses," the cluster's topic is derived from the two keywords' relationship. Cluster 11 included four keywords; with the presence of topics such as "Pneumonia," which is the most frequent keyword of this cluster, and "RSV," it can be said that this cluster has addressed the issue of respiratory syncytial virus pneumonia, hospitalization, and mortality caused by this disease. There are three keywords in Cluster 12 related to swine in uenza; "H1N1" is the most frequent keyword with a frequency equal to 665 and explains the cluster's subject. Cluster 13 included four keywords, "Antibody" is the most important of which with a frequency of 307, and considering the presence of relevant keywords, it can be said that this cluster discusses the subject of "antibodies and determining their level against in uenza viruses." Cluster 14 also possessed two keywords: the laboratory methods "RT-PCR" and "Diagnosis"; it can be said that the cluster refers to the diagnosis of in uenza. Cluster 15 involved three keywords, "Epidemiology" and "Outbreak," which are the most important based on rankings, respectively. According to two high-ranking keywords, it can be declared that the topic of this cluster is related to the epidemic situation and the outbreak of in uenza. In Cluster 16, there are four keywords, "Swine In uenza virus" is the most important of which is based on rankings. In this cluster, the subject is clear. Cluster 17 is the largest cluster of topical in uenza areas with 21 keywords. This cluster is considerably varied in terms of topics not to be limited to one speci c area. However, considering the most important keywords of this cluster, namely "Pandemic In uenza," "Seasonal In uenza," "Public Health," and "Infection," it can be said that this cluster is related to "seasonal in uenza pandemic." Cluster 18 included six important and high-ranking keywords such as "Avian In uenza virus," "H9N2," and "H7N9". Hence, this cluster is related to the avian in uenza virus. Cluster 19 possessed two keywords, "Immunohistochemistry" and "Pathology." Since immunohistochemistry is one of the most important diagnostic methods in medicine, especially in Pathology, the subject of this cluster is the pathology of in uenza. Cluster 20 had six keywords, including the rst top keyword of this topical area, namely "Avian In uenza." By all means, the names of several subtypes of in uenza virus such as "H5N8" and "H5N2" are also mentioned in this cluster, which increased the thematic and content a nity with Cluster 18. Repetition of high-ranking keywords such as "Avian In uenza" and "H5N1" indicated the cluster's topical area to be related to avian in uenza. Cluster 21 included seven keywords. Due to the presence of keywords such as "Oseltamivir," "Amantadine," "Drug resistance," "Resistance," "Peramivir," and "Neuraminidase inhibitors," the subject of this cluster can be related to antiviral drugs. In total, among 21 clusters in this article, six clusters (1, 2, 5, 10, 14, and 19) have two keywords; ve clusters (3,7,9,12, and 15) included three keywords; two clusters (4 and 8) possessed ve keywords; three clusters (11, 13, and 16) involved four keywords; two clusters (18 and 20) had six keywords; two clusters (6 and 21) included seven keywords, and one cluster (17) had 21 keywords.

Density and Centrality of Clusters Resulting from Co-Word Analysis of In uenza Topical Area
Co-word analysis, using UCINET for each of the 21 clusters, a frequency matrix was created separately, then a correlation matrix was created, and the concepts of centrality, ranking, and density of the network of each cluster were calculated, and the average of each cluster was obtained. (35,36). The in uenza topical area is presented based on data on each of the 21 clusters' centrality and density (Table 3). Density and coherence are two network measurement methods that mostly describe the whole network instead of depicting individual nodes (37). According to Table (3), Clusters 1, 20, and 9 have the highest density, and Clusters 20, 9, and 21 have the highest centrality. The centrality and density of each of these topical clusters can be represented in a strategic diagram.
Degree of maturity or coherence of clusters of the in uenza topical area in the strategic diagram Figure (3) indicated the strategic diagram of the clusters obtained from co-word analysis in the eld of in uenza. According to the clusters' average centrality and density, the diagram's origin was set at 14.86 and 18.07, respectively. In a strategic diagram, the X-axis indicated the centrality of the ranking and the interaction strength of each cluster in the study area, and the Y-axis indicated the density and the internal relationship in a particular research area. The higher the density of a cluster, the more it will be able to preserve and develop itself (34). Each cluster is located in one of the four quadrants of the diagram. The rst quadrant included mature and central clusters; the second quadrant involved developed but separated clusters, the third quadrant included emerging or declining clusters, and the fourth quadrant involved central but undeveloped clusters. The higher the density of a topical cluster, its maturity is higher, and it is more capable of maintaining and developing itself (the vertical axis also indicated the internal relationship of a research area), and the higher the centrality of a cluster, it is more central and essential in that research area (35, 38, 39, & 40).

Discussion
Co-word analysis is an e cient method of scientometrics that determined the semantic structure, evolution, and dynamics of the products around a topical area over time by analyzing the main topical areas (38, 39 & 42). In uenza virus remains a continuous and severe threat to public health worldwide, and its prevention and treatment have always been a major international issue (43). In the present research, hidden patterns and emerging in uenza events in international research indexed in WOSCC were discovered using the co-word analysis techniques and science visualization software to visualize the intellectual structure of research over twenty years (2000-2019). The research results showed that the publication trend of scienti c documents between 2000 and 2011 has always been upward. The results indicated that this upward trend and its peak in 2011 was due to the passaging of the H1N1 pandemic in 2009 and the World Health Organization's warning about the swine in uenza pandemic (39).
The growth of international scienti c productions between 2009 and 2011 in the present research is similar to the results of the studies conducted by Ziegler (41), Batcha (24), and avian in uenza research by Rathinasabapathy and Kopperundevi (28). As reviewed, other scientometric techniques have been applied in the studies as mentioned above and also H1N1 research area conducted by Preeti and Rishabh (27) and the scientometric study of in uenza carried out by Fricke et al. (25), which showed the applicability and ease of use of the techniques as mentioned earlier and the proper presentation of a holistic picture of the studied area.
"Avian In uenza," "In uenza Vaccine," "Vaccination," "Hemagglutinin," and "H5N1," respectively, with more frequency of repetition in in uenza topical area, have been considered by researchers, have involved a large share of studies. Moreover, they have had a central role in the research and the topical map resulting from research; thus, they have formed the core topics based on the most frequent keywords in the in uenza topical area (which has not been addressed in other scientometric studies in this eld). On the contrary, keywords such as "Seasonal In uenza Vaccine," "Pathology," "H5N8", "H5N2," and "Mathematical Model" have included the surrounding topics with a lower rate of co-occurrence.
In the study of avian in uenza conducted by Selvaraj (26) In the present investigation, the core keyword was determined, the strategic diagram was drawn, and the in uenza topical area's intellectual structure was demonstrated using co-word analysis. The studies conducted by Makkizadeh and Bigdeloo (21), Wei et al. (22), and Makkizadeh (23) with the approach as mentioned above to analyzed the topical areas in medical sciences are consistent with the approach of the present research.
Clusters 20, 9, and 21 had the highest centrality; thus, these clusters' subjects were in great connections with other nodes, have higher centrality and strength, and were particularly important in the network.
Clusters 1, 20, and 9 had the highest density; hence, they had high coherence and internal correlation.
According to the strategic diagram, the subjects addressed in Clusters 20,9,21,12 (avian in uenza, in uenza vaccine and its effects on immunity, antiviral drugs, and swine in uenza) placed in the rst quadrant were the primary and developed clusters with a pivotal role that are considered to be welldeveloped. Clusters 1 and 5 (immunogenicity, safety, and biosecurity against in uenza) were not pivotal but well-developed and placed in the second quadrant.
Clusters involving emerging, declining, or neglected topics are located in the diagram's third quadrant. The subjects of Clusters 7,15,11,8,17,16,14,10,19, & 2 were lower in terms of centrality and density compared to the other clusters and had a marginal status. The high-frequent keywords "Epidemiology" and "Outbreak" were in Cluster 15; "Pneumonia," "Mortality" and "Hospitalization" are in Cluster 11; "Cytokines" is in Cluster 8; "Pandemic In uenza," "Seasonal In uenza," "Public Health," and "Infection" were in Cluster 17, the keyword "RT-PCR" is in Cluster 16," and also "In uenza-Like Illness" was in Cluster 10, which indicated that researchers' interest to these issues. The third quadrant, in the order of centrality, included the topics innate immune, epidemic and outbreak of in uenza, respiratory syncytial virus pneumonia, pathogenesis and immunopathogenesis of infection by the virus, seasonal in uenza pandemic, swine in uenza virus, in uenza diagnosis, in uenza-like illness, and respiratory viruses, pathology of in uenza and clinical trials of vaccines and were placed in the in uenza topical area. More research was needed to reach the expected maturity. The comprehensive, extensive, and immature topics aggregated in the fourth quadrant. The topics of Clusters 3, 4, 18, and 6 (prevention and immunization with the vaccine, vaccination, avian in uenza virus, and the role of protective antibodies against in uenza) were in the fourth quadrant of the diagram. These were central topics and had remained undeveloped and immature due to researchers' lack of acceptance of these subjects. Hence, the fourth quadrant subjects are studied with the least degree of impact on the area. Despite the presence of highfrequent and important keywords "Avian In uenza virus" and "Vaccination" in this quadrant, it seemed that they should be part of the main cluster, but they are in the fourth quadrant; the reason is the relatively low density in this cluster, which showed the weak connection of inner topics.
For conducting more effective research, it is suggested that a focus be made on cases related to the "in uenza vaccine" and quantitative and qualitative analysis in an independent study to be conducted based on scientometric techniques in the global outputs of science in this eld. According to the results obtained from the strategic diagram, for targeting the research, researchers or research planning committees should focus on the concepts of the third and fourth quadrants of this research to cause scienti c improvements in this topical area while attempting to make the subjects mature. These concepts can also be applied to expand related centers' educational and research activities in a practical and applied way. The present study has been carried out by applying co-word analysis and focused on researchers' topics. The suggestion is that other similar researches to be performed by co-citation analysis to discover the most proli c and effective authors in this eld.

Conclusion
Specialists and research centers in the eld of in uenza have always made the results of their research available in the form of scienti c documents in the world's top citation databases. Providing a snapshot of commonly used and similar topics and concepts in the present study, while providing hidden patterns, reveals emerging international events on in uenza over the past twenty years that the results will potentially and signi cantly help governments, communities, and research centers to understand research advances. Optimization and management of future research topics and useful and e cient scienti c policy-making and monitoring on new scienti c and research activities will also be provided in this process. The present study is the result of independent research conducted with no nancial support. The authors declare that there is no con ict of interest.
Ethics approval and consent to participate Studies involving animals "Not applicable." Consent to publication "Not applicable."

Availability of data and material
Database and advance search strategy for data collecting is described in the methodology section, and researchers can use the strategy to get data. So the data is made available.

Competing interests
The present article is the result of an independent study conducted with no nancial support. The authors declare that there is no con ict of interest.

Funding
The present paper is the result of independent research conducted with no nancial support. The authors declare that there is no con ict of interest.

Authors' contributions
Dr. Farshid Danesh: Conceived and designed the research, Collect the data, and wrote the paper.
Somayeh GhaviDel: Collect the data, contributed the analysis tools, and analyze the data.