Scientific collaboration in Zika : identification of the leading research groups and researchers via social network analysis

Devido à associação entre Zika e microcefalia, o Brasil recebeu atenção neste cenário. A situação de emergência exigiu rapidez e esforço coletivo dos pesquisadores de todo o mundo, e a Ciência se apressou nas investigações e publicação dos resultados. A partir das interações formadas, criou-se e se disseminou conhecimento científico. Publicações ainda são a melhor forma de divulgar o conhecimento científico. Através delas é possível registrar os progressos realizados em um campo de estudos e observar como os cientistas colaboram entre si para conduzir avanços à medida que novos conhecimentos e tecnologias são engendrados. Um modo eficaz de mapear esses avanços é analisar as Redes Sociais (redes de relacionamentos e colaboração) dos cientistas, já que atualmente a colaboração constitui uma característica intrínseca da Ciência moderna. Desse modo, a coautoria em publicações se apresenta como um importante indicador de colaboração científica na compreensão dos progressos realizados em diversas áreas da Ciência. Este trabalho objetiva, por um método generalizável, mapear e analisar a Rede Social Científica formada no domínio de Zika, mostrando como os cientistas colaboraram entre si para conduzir os principais avanços de pesquisa, identificando os principais grupos de pesquisa em Zika, além dos pesquisadores mais influentes. Para isso, utilizaram-se técnicas de Análise de Redes Sociais nas redes de coautoria formadas entre os anos de 2015 e 2016. Os dados deste estudo sinalizam que a influência de um pesquisador em Zika é basicamente motivada por três fatores: (a) quantidade de publicações; (b) parcerias diversificadas; e (c) os vínculos estabelecidos com os pioneiros da área.


Introduction
The Zika virus (ZIKV) epidemic emerged in 2015 in Brazil as a new phenomenon that continues to demand responses by science on unprecedented issues such as the significant number of microcephaly cases and other neurological alterations in newborns.Zika was previously confined to a limited region of Africa, with a history of benign, uncomplicated clinical evolution 1 .It was only after the outbreaks on Yap Island, Micronesia, in 2007 and later in French Polynesia in 2013 and Brazil in 2014 that it became urgent to study the disease and seek answers to this international health problem.

Related studies
One pioneering study on scientific collaboration networks was by Newman 15 , who explored the PubMed/MEDLINE database and extracted publications and analyzed SSN in various themes in Biomedicine using metrics that examine networks from a macro/global perspective.The study analyzed 2,163,923 publications and identified 1,520,251 researchers.The study also showed a mean of 6.40 articles per researcher, 3.75 researchers per article, and mean collaboration of 18.10.Contrasting with the work by Newman, the current study analyzed SSN from a multidimensional perspective, considering different levels/metrics, in addition to the global metrics explored by that author.
Freeman 16 demonstrated mathematically how it is possible to calculate the centralization of vertices based on the absolute or relative position in relation to other vertices in a social network, allowing the attribution of scores (degree, closeness, and betweenness) for each vertex.Yan & Ding 17 proposed a method to calculate researchers' influence in SSN, applying the centralization metrics as proposed by Freeman 16 together with PageRank 18 in the field of Library Science and Information Science to analyze the network structure at the "micro" level, that is, examining in depth the SSN structure aided by the four metrics.Meanwhile, Liu et al. 19 used the four previous metrics similarly, but the authors proposed a new metric similar to PageRank to assist their analyses, but taking edge weights into account.Their method was applied to the networks of authors that published in the main digital libraries in Computer Science domain.As with Yan & Ding 17 and Liu et al. 19 , this study examined in detail the structural aspects of SSN at the micro level (considering researchers and their relational dynamic), but in the current study's method, the combined use of metrics allows the more accurate identification of the most influential researchers.
In the Brazilian scenario, Morel et al. 12 studied scientific productivity and the ways co-authorships are formed between Brazilian researchers in the Web of Science database.Based on this, they were able to map the SSN and analyze the formation of clusters of authors that published international articles on seven neglected tropical diseases from 2001 to 2008.Using keywords extracted from the articles, the authors inferred important co-authorships between the researchers, such as clusters formed in dengue research and the bridges between institutions and groups in tuberculosis research.Besides the way Morel et al. 12 analyzed co-authorships, another difference between that study and the current one is the fact that it automated the process of data retrieval, treatment, and integration and construction of the SSN.
Albuquerque et al. 11 analyzed scientific collaboration between Brazilian researchers that participated in the National Institute of Science and Technology for Cancer Control (INCTCC).The authors conducted social network analysis using a multidimensional model, also analyzing the time series of publications by the group members based on information retrieved from the Lattes and PubMed platforms.Unlike the method used here, which can be generalized to other scenarios and databases, the method presented in that study is limited to the Brazilian scenario, since it uses the Lattes database to extract information and map relations in SSN.

Building the Scientific Social Network
The SSN analysis in this study employed connectivity 20 and centrality 16 metrics, namely degree, betweenness, and closeness, to study how and with whom the researchers establish co-authorships.The study also used a fourth centrality metric, PageRank 18 , which measures the relevance of network nodes based on the relevance of other nodes linked to them.
Centrality metrics were chosen according to the definition of "Prestige" in Wasserman & Faust 20 .Degree Prestige is associated with the number of direct links to a researcher in the network.The more links the researcher has in the SSN, the higher his or her Degree Prestige.Closeness Prestige considers as most "central" the researcher with a shorter mean distance in relation to all the others in the network.Researchers that collaborate with more central researchers in the SSN have higher Closeness Prestige.Betweenness Prestige considers as the researchers with the higher prestige those that act as bridges, connecting different research groups.In addition, higher prestige was attributed to the researchers oftener referenced in the SSN, using the PageRank metric 13 .
Cad. Saúde Pública 2019; 35(3):e00220217 The process used (Figure 1), adapted from Maia & Oliveira 13 and Maia & Yagui 21 , is responsible for: (i) retrieving data on the researchers in publications on Zika extracted from PubMed; (ii) building a co-authorship SSN based on the retrieved data; (iii) applying SNA metrics to this SSN; and (iv) identifying important researchers and their roles in the Zika SSN, based on their productivity and influence in the research networks to which they belong.
Using the search mechanism for PubMed (https://www.ncbi.nlm.nih.gov/pubmed/advanced) and as search string the term "Zika" applied in the filters "title", "abstract" and "text", on December 21, 2016, data were extracted from 1,932 publications on Zika.To use these data, a workflow was prepared with the Knime tool (https://www.knime.org/about) in order to integrate data on authors and publications to generate the SSN and upload it into the SNA tool called Cytoscape (http:// www.cytoscape.org/what_is_cytoscape.html).Next, a collaboration graph was generated to view the resulting network, in which the authors are the nodes and the publications are the edges.This allowed applying SNA metrics and analyzing the SSN at three different levels: global, local, and individual.

Global analysis
At this level of analysis, the behavior of global publishing on Zika was analyzed based on a preliminary bibliometric analysis correlating data on researchers and publications.Table 1 summarizes the results of this analysis.
As shown in Table 1, this analysis identified 6,808 researchers in the Zika SSN, in which a researcher published an average of 1.49 articles, the articles have an average of 5.20 researchers, and a researcher collaborates, on average, with 6.12 researchers.
Observing the results of the study by Newman 15 and the current study's preliminary bibliometric analyses, some comparisons can be made.For example, the average number of publications per    researcher is low when compared to the overall publications in Biomedicine.Only 37 researchers among the 6,808 that were identified had published more than 10 articles on Zika.This indicates that until a few years ago the theme received little attention from the biomedical community, which can be explained by the fact that the interest in studying the disease in greater depth only emerged after the outbreaks in 2013 22 .On the other hand, the average number of researchers per publication is 5.20, namely 39% higher than the mean observed in Biomedicine publications.By way of example, this study identified 322 publications in which 10 or more authors participated in the research, that is, a sixth of the entire sample.This indicates that the theme tends to form research groups with many members.Finally, the mean collaboration of 6.12 is low (three times lower) when compared to research groups in Biomedicine.This shows that ZIKV researchers tend to collaborate less at the global level.
In the next stage, in order to facilitate identification of the most influential researchers, the network's isolated components and weak links were eliminated (researchers that published without coauthors and links with only one publication in common).The following parameters were used for this purpose: (i) weights for the nodes/researchers, where the weight corresponds to that node's number of edges; (ii) weights for the edges, where the weight corresponds to the number of publications in common; (iii) the occurrence of an edge conditioned on the existence of two or more publications in common; and (iv) removal of isolated nodes from the SSN.The graph visualization thus becomes less polluted and the identification of the important components becomes easier.
After the adjustment, a collaboration graph was projected in which it is possible to identify 1,025 nodes and 3,608 edges, or 8,650 edges if one considers the weight as a function of those that are repeated, that is, 8,650 publications in common were mapped among the 1,025 researchers.
Having done this, it was possible apply connectivity metrics in the SSN to identify the groups of nodes/researchers that stand out in the observed structure.

Local analysis
At this level of analysis, the largest components/clusters were identified, with 208, 133, and 96 nodes, corresponding to 20.29%, 12.97%, and 9.36% of all the researchers in the SSN.Among the other components identified, the fourth, fifth, sixth, seventh, and eighth largest had 37, 23, 22, 22, and 21 nodes, respectively.All the other components had less than 20 nodes each.Table 2 shows the connectivity metrics for the eight largest clusters in the SSN.
Based on these numbers (especially nodes and network diameter) for this study, only the three largest clusters identified in the SSN (hereinafter subnetwork 1, subnetwork 2, and subnetwork 3) were considered in the individual analysis (437 nodes).Figure 2 illustrates the distribution of the scientific collaborations in Zika and highlights the three main clusters/subnetworks that were identified.

Individual analysis
After identification of the most important subsets of researchers, the centrality metrics explained in section Building the Scientific Social Network were applied to analyze the individual properties of the three subnetworks and identify the most influential researchers.The analyses at this level were adapted from the work by Maia & Oliveira 13 , which incorporated the concepts of centrality applied to the work by Freeman 16 , Yan & Ding 17 , and Liu et al. 19 .Based on these proposals, the current analysis extracted the degree, closeness, and betweenness to find the most central nodes, and PageRank was used to compare these nodes with the most frequently referenced nodes.

Research productivity analysis
An important initial criterion is the Number of Publications (NP) by each researcher, since quantification of productivity (despite criticisms) is still an essential factor in the academic community for determining whether a researcher is leading progress in his or her field of work or in specific areas, such as Zika.Thus, each researcher's NP was extracted and a ranking was created based on the number times that researcher published.This ranking only took into account those who had published at least five times, excluding the researchers with lower output and reducing the scope of the next analysis.In addition, as a way of facilitating the identification of the most productive researchers and improving the numbers' visualization, four color categories were defined based on each researcher's NP, according to the criteria listed in Table 3.
Having defined these criteria, we found that among the researchers who have published the most, 133 are in the red category, 24 in the purple, 21 in the blue, and 16 in the green.Table 4 shows the researchers with the most publications (category green) in the three main clusters/subnetworks.
The results in Table 4 show that these names are actually researchers that belong to networks of scientific collaboration with strong geopolitical/institutional references.Well-defined clusters are seen in this group, for example, the one formed by researchers from the Institut Louis Malardé -ILM (Tahiti/French Polynesia), consisting of Didier Musso (MUSSO D) and Van-Mai Cao-Lormeau (CAO-LORMEAU VM) and Isabelle Leparc-Goffart (LEPARC-GOFFART I), that belongs to the French National Reference Center for Arboviruses -NRCA and to the Institut de Recherche Biomédicale des Armées -IRBA (Marseille/France).
These researchers began to publish in 2014, following the ZIKV outbreak in French Polynesia in 2013.Didier Musso and Cao-Lormeau have a strong partnership with Duane Gubler from Duke-NUS Medical School (Singapore) and from Partnership for Dengue Control (Lyon/France), which although not among the most productive researchers, nevertheless reinforces the link with another French institution in this co-authorship network.

Figure 2
Collaboration graph of the Scientific Social Network on Zika, highlighting the three main clusters of researchers that were identified (subnetwork 1, subnetwork 2, and subnetwork 3).
Note: the co-authorship graph with the Zika researchers' names is available at: https://luisfmpm.github.io/CSP/.The co-authorship graph also highlights the weights applied to the nodes and edges, in which the size of the nodes varies according to each researcher's number of co-authorships, and the size of the edges varies according to the number of co-authorships between two researchers.
This group of 11 researchers also features a strong cluster from CDC (Atlanta/United States), namely Mark Fischer (FISCHER M), Denise J. Jamieson ( JAMIESON DJ), Margareth A. Honein (HONEIN MA), and J. Erin Staples (STAPLES JE).Based on the capillarity of CDC in terms of partnerships and research development, this cluster showed highly significant productivity based on publishing.Mark Fischer, for example, was one of the first to publish on the ZIKV epidemic on Yap Island in 2007 23 .

Table 3
Color categories based on the number of publications (NP).Scott Weaver (WEAVER SC) and Nikos Vasilakis (VASILAKIS N) from the University of Texas -UT (United States) lead another important group in publishing on Zika.In addition to UT, other smaller groups from Washington University, with Michael S. Diamond (DIAMOND MS), from Johns Hopkins University, and others confirmed the strong participation by the United States on Zika research.

Red
Cheng-Feng Qin (QIN CF), from the Department of Virology at the Beijing Institute of Microbiology and Epidemiology (Beijing/China), shows strong participation in the network of publications on Zika, mainly with Chinese partnerships.

Ranking of Freeman's metrics
In this analysis, each researcher's degree, closeness, and betweenness were extracted.Next, the researchers were ranked individually (on their subnetworks) based on these measures, where the higher their score in a metric, the better their ranking in that metric.Only the 100 highest ranking in each metric were considered in this process.
We then totaled the researchers' ranks in the three metrics, where the most influential ones are listed in ascending order according to the values in the column "Score".Researchers with low productivity (Table 3) or low scores, namely betweenness or closeness or degree below the first 100, were excluded from this ranking.Next, a specific ranking was created for the network's researchers with less centrality (low scores in one of the three metrics), according to the same criteria as in the previous ranking.These researchers (underlined) appear right below the more central researchers in the subsequent tables.
Tables 5, 6, and 7 show the highest-ranking researchers in the three subnetworks according to these criteria, in addition to the color/productivity categories (Table 3).The degree and the number of publications were used as a tie-breaking criterion.

Comparative ranking (PageRank)
In this stage, PageRank was calculated for each researcher.According to the criteria for prestige defined in section Building the Scientific Social Network, the higher the PageRank value, the more the researcher is related to high prestige or widely referenced nodes.This metric was thus used complementarily to verify the reliability of the centrality ranking, since higher prestige researchers should also be expected to score better on PageRank.
With this in mind, the rankings in Tables 5, 6, and 7 were compared to the 100 most productive researchers (Table 3) in the three subnetworks in PageRank (Tables 8, 9, and 10).Only 2 researchers from the Freeman ranking did not appear in the comparative ranking.Meanwhile, 9 researchers from the comparative ranking did not appear in the Freeman ranking.However, in their respective clusters, these 11 nodes are linked to the high prestige nodes in the Freeman ranking.Comparative Tables 8, 9, and 10 show the researchers listed in descending order according to PageRank.
Thus, based on the productivity (publications) and prestige criteria (betweenness, closeness, degree and PageRank), of the 437 nodes identified in the individual analysis, the 106 most influential researchers were mapped, of which 54 in subnetwork 1, 34 in subnetwork 2, and 18 in subnetwork 3, as shown in Figures 3, 4, and 5.

The most influential researchers in the three subnetworks
Concerning the mapping of researchers according to their influence in the Zika SSN during the study period, the results support the idea that the most productive researchers are also the most influential.

• Subnetwork 1
Subnetwork 1, the largest cluster of researchers identified by this study, is characterized by the strong participation by Chinese researchers, with Cheng-Feng Qin (the most influential in the subnetwork 1).For example, according to Table 5, 8 of the 14 most influential researchers are Chinese.
Scott Weaver and Nikos Vasilakis, from the UT, who ranked first in Tables 5 and 8, lead a diverse group consisting of Brazilian researchers and several Chinese researchers who conduct studies in the United States and also in Beijing/China.In these co-authorship links, an important node is Pei-Yong Shi (SHI PY), from UT, connecting to the group in China via Cheng-Feng Qin.
Co-authorship with Brazilian researchers occurred through the partnership with Albert Icksang Ko (KO AI) from the Yale School of Public Health (United States) who is a collaborating researcher with the Gonçalo Moniz Institute (IGM) of the Oswaldo Cruz Foundation (Fiocruz), in Salvador, Bahia, Brazil.
Subnetwork 1 is the one with the most ranked Brazilian researchers.They are distributed across research groups linked to two key state capitals for the epidemic, Salvador and Recife, both in Northeast Brazil, and with Fiocruz Research Centers, namely the IGM in Salvador, Bahia, and the Aggeu Magalhães Institute (IAM) in Recife, Pernambuco.
Guilherme de Sousa Ribeiro (RIBEIRO GS) of IGM/Fiocruz is the highest-ranking Brazilian researcher among the most influential researchers.A closer look at Figure 3 shows that he is a bridge between several groups from important nodes, which also explains his high betweenness score.These results are due to his partnership with UT and Albert Ko, yielding 7 publications for him in co-authorship with Scott Weaver and Nikos Vasilakis, Gúbio Soares Campos (CAMPOS GS) (Federal University of Bahia -UFBA), Bruno de Paula Freitas (DE PAULA FREITAS) (Roberto Santos General Hospital -HGRS), Mittermayer Galvão Reis (REIS MG), and Federico Costa (IGM/Fiocruz), who published 4 times on ZIKV.
Close to this group, there is also a group of 7 nodes to which Gúbio Soares (UFBA) belongs, the bridge between this group and that of Guilherme Ribeiro.With 7 publications, Gúbio has a Cad.Saúde Pública 2019; 35(3):e00220217

Table 5
Ranking of the most influential researchers in subnetwork 1 based on the Freeman metrics, which also covers the color categories based on the number of publications (Table 3).Ranking of the most influential researchers in subnetwork 2 based on the Freeman metrics, which also cover the color categories based on the number of publications (Table 3).Ranking of the most influential researchers in subnetwork 3 based on the Freeman metrics, which also cover the color categories based on the number of publications (Table 3).from UNIFESP, Bruno Freitas from HGRS, Liana Maria Vieira de Oliveira Ventura (VENTURA LO), coordinator of the Department of Pediatric Ophthalmology and Strabismus from the Pernambuco Eye Hospital, and Albert Ko from IGM/Fiocruz.This group with the most links to Brazilians also features Vanessa Van Der Linden of HBL, with 8 publications, and Regina Ramos from UFPE, with 9 publications.Both have high betweenness and PageRank scores, indicating their links to important nodes (explained in the previous paragraph) and their action as mediators between other nodes of researchers that are more isolated in the network.Camila Ventura from UNIFESP, with 10 publications, Marli T Cordeiro (CORDEIRO MT) from IAM/Fiocruz, with 9 publications, Maria Ângela Wanderley Rocha (ROCHA MA) from Oswaldo Cruz University Hospital (Recife), with 7 publications, Bruno Freitas from HGRS, with 5 publications, and Laura Cunha Rodrigues (RODRIGUES LC) from the London School of Hygiene and Tropical Medicine -LSHTM (United Kingdom), with 11 publications, complete the body of influential researchers in subnetwork 1, but most of whom did not score on the closeness metric, indicating that this group has a more isolated position in the network (as indicated in section Ranking of Freeman's Metrics).

Researcher
Interestingly, the majority of these researchers are linked to the Microcephaly Epidemic Research Group (MERG), coordinated by Dr. Celina Maria Turchi Martelli (MARTELLI CM), who does not appear in the Freeman ranking but scored well in the PageRank metric.This research group belongs to IAM/Fiocruz and works in partnership with various Brazilian and international institutions, including University of Pernambuco -UPE, UFPE, Pernambuco State Health Department -SES-PE, LSHTM (United Kingdom), University of Pittsburgh (United States), Altino Ventura Foundation -FAV, Disabled Child Care Association -AACD, and the Professor Fernando Figueira Integral Medicine Institute -IMIP.
Among many articles produced from 2015 to 2016, one was quite special.In June 2016, the group published an article entitled Microcephaly in Infants, Pernambuco State, Brazil, 2015 24 , of major international importance, since it reported the first research confirming in a case-control study that the microcephaly epidemic resulted from congenital ZIKV infection.

Table 8
Comparative ranking of subnetwork 1, which includes the researchers' PageRank position (PRP), the Freeman ranking position (FRP) and the color categories based on the number of publications (Table 3).Note: this ranking also includes 2 researchers that did not score on PageRank (in bold), 8 that did not appear in the Freeman ranking (in italics), and the 15 researchers (underlined) that are influential, but less central according to the Freeman ranking.

Researcher
However, the media credits this discovery to CDC, which published a press note in April 2016 stating that "[they] ...have concluded, after careful review of existing evidence, that Zika virus is a cause of microcephaly and other severe fetal brain defects.In the report published in the New England Journal of Medicine, the CDC authors describe a rigorous weighing of evidence using established scientific criteria" 25 .
The work by the MERG was acknowledged by Nature, one of the world's most prestigious scientific publications, which listed Dr. Celina Turchi Martelli as one of the ten most notable people in science in 2016.
Thus, the analysis of subnetwork 1 clearly shows the leadership of China and the USA in stateof-the-art research in Zika.Although Brazil witnessed the worst epidemic of microcephaly with the occurrence of neurological complications in newborns and produced important scientific research on Zika, Brazilian researchers did not rank in any leading positions.

• Subnetwork 2
The most influential researchers in this subnetwork, based on the centrality metrics, all belong to the CDC.Denise Jamieson, Titilope Oduyebo (ODUYEBO T), and Margareth Honein are the three most influential researchers in these rankings and belong to the same research group -The Pregnancy and Birth Defects Task Force for CDC Zika Virus Response, with major scientific output on the theme.

Table 10
Comparative ranking of subnetwork 3, which includes the researchers' PageRank position (PRP), the Freeman ranking position (FRP) and the color categories based on the number of publications (Table 3).Note: this ranking also includes 1 researcher that does not appear in the Freeman ranking (in italics).

Table 9
Comparative ranking of subnetwork 2, which includes the researchers' PageRank position (PRP), the Freeman ranking position (FRP), and the color categories based on the number of publications (Table 3).The study of the Zika epidemic in Puerto Rico appears in this network, again demonstrating the formation of subnetworks through geopolitical and institutional relations.

Researcher
The co-authorship groups are quite interchangeable.Based on the most influential first authors, some co-authorships can be identified: • Denise Jamieson, Sonja A. Rasmussen (RASMUSSEN AS), Margareth Honein, Lyle R. Petersen (PETERSEN LR), Erin Staples, Mark Fisher.
• Tyler M. Sharp (SHARP TM), Aidsa Rivera (RIVERA A), Brenda Rivera-Garcia, also in the scientific production on Zika in Puerto Rico.

Figure 3
Collaboration graph of the subnetwork 1, highlighting the most influential researchers in this cluster according to the color categories defined in Table 3.

Figure 4
Collaboration graph of the subnetwork 2, highlighting the most influential researchers in this cluster according to the color categories defined in Table 3.
• Titilope Oduyebo, Robert Lanciotti (LANCIOTTI RS), Amy Lambert, Julu Bhatnagar and Kleber Giovanni Luz.This group of coauthors includes a researcher from Federal University of Rio Grande do Norte, another state from Northeast Brazil that also experienced the epidemic, like Bahia and Pernambuco.Kleber Luz appears in subnetwork 2 as coauthor, recording the first cases of microcephaly in the state of Rio Grande do Norte 26 .However, this researcher does not appear among the most influential.

Figure 5
Collaboration graph of the subnetwork 3, highlighting the most influential researchers in this cluster according to the color categories defined in Table 3.
Compared to subnetwork 1, this cluster is denser and shows higher correlation between the four centrality metrics analyzed.
As shown in Tables 7 and 10, the principal nodes are Didier Musso and Cao-Lormeau from ILM (Tahiti); and Isabelle Leparc-Goffart from NRCA-IRBA (Marseille).These three authors began to publish on Zika in the year 2014, when the outbreak occurred in French Polynesia 27 .
One can observe two distinct groups in this subnetwork, one led by Didier Musso and Cao-Lormeau and the other by Leparc-Goffart.
The strongest co-authorship group in this subnetwork is linked to Didier Musso and Cao-Lormeau, with strong participation by Anita Teissier (TEISSIER A), also from ILM, and Henry-Pierre Mallet (MALLET HP) from the French Polynesia Ministry of Health.
Three authors from Swiss institutions, Alice Panchaud (PANCHAUD A) of Geneva and David Baud (BAUD D) and Manon Vouga (VOUGA M) of Lausanne are linked to Didier Musso.

Final remarks
This study aimed to identify the principal research groups in Zika, as well as the researchers with the most publications and the highest prestige/status on this subject.To achieve this, the study analyzed scientific interactions in the Zika SSN at three levels: global, local, and individual.
The global analysis provided a macro view of this field of study via bibliometric indicators.According to this analysis, compared to other studies conducted in the PubMed/MEDLINE database on various themes in Biomedicine, collaboration and publishing output by Zika researchers is smaller than other themes in public health, reflecting the limited importance assigned to ZIKV in the international scenario until the recent epidemics 22 .
The local analysis identified the three main clusters of researchers in the SSN, with 208, 133, and 96 nodes, respectively, which include the most important research groups in this area.
The individual analysis identified the 106 most influential researchers -in terms of activity and collaboration -in research on ZIKV.
The observation of these names and research groups shows that they are the researchers who are spearheading significant strides on Zika research, exercising leadership in renowned research institutions and coordinating joint efforts between different institutes and research groups, favoring the exchange of knowledge.
The study also found that in general the researchers with the most publications were among the most influential in their respective subnetworks.In this sense, it is interesting to note that many of these cases (researchers with high influence and productivity) are people that belong to well-defined research groups with strong geopolitical and institutional references, responsible for considerable strides in studies on ZIKV.These results stand out when analyzing who are the researchers in Table 4, that shows the 11 most productive ones in the 3 subnetworks.
The study's data signal that a researcher's influence in Zika SSN is basically determined by three factors: (a) whether their efforts are translated as results corroborated by their publications (productivity); (b) whether their publications are the result of partnerships between different research groups, favoring exchange of knowledge; and (c) the amount of links established, more specifically with groups/researchers that are or have been pioneers in the search for answers to solve the problem 28 .
Although Brazilian researchers have played a relevant role in identification of neurological damage related to congenital Zika and in the subsequent investigation, the Brazilian research networks do not stand out in the analyses performed here.
Importantly, thus far no national or international mapping or studies have been published on how scientific interactions on the disease occurred (studying in depth the Zika SSN).This study is thus pioneering on the subject as an important element for studying the evolution of research on Zika.The methodology can also be applied to the study of other areas of science.
The results can be used to understand and improve the scientific collaboration between research groups in ZIKV.
Future studies should continue with analysis of the impact and social recognition of researchers and their discoveries through alternative metrics (altmetrics 13

Table
Global analysis: bibliometric data of the Scientific Social Network on Zika.

Table 2
Local analysis: connectivity metrics for the eight largest clusters in the Scientific Social Network on Zika.

Table 4
Researchers who have published the most in the 3 subnetworks.

Table 6
, both from the Department of Ophthalmology and Visual Sciences at the São Paulo Medical School, Federal University of São Paulo (UNIFESP), with 9 and 7 publications, respectively, with studies on visual disorders in children with microcephaly.Both also scored well on the Freeman ranking and PageRank, which translates as partnerships in co-authorships they developed with researchers from Recife and Salvador.This partnership includes Vanessa Van Der Linden (VAN DER LINDEN V) from Barão de Lucena Hospital -HBL (Recife), Regina Coeli Ferreira Ramos (RAMOS RC) from Federal University of Pernambuco -UFPE, Camila Ventura (VENTURA CV) Cad. Saúde Pública 2019; 35(3):e00220217