Abstract:

The association between Zika and microcephaly drew international attention to Brazil. The emergency situation demanded speed and collective effort by researchers worldwide, and Science was quick to investigate the disease and publish the results. Scientific knowledge was created and disseminated through collaboration in this process. Publications are still the best way of disseminating scientific knowledge. They allow to record progress in a field of studies and observe how scientists collaborate to produce advances as new knowledge and technologies are generated. An effective way to map such advances is to analyze scientists’ Social Networks (relationship and collaboration networks), since collaboration is currently an intrinsic characteristic of modern science. Co-authorship of publications is thus an important indicator of scientific collaboration for understanding progress in various areas of Science. The current study aimed to use a generalizable method for mapping and analyzing the Scientific Social Network formed in the domain of Zika, demonstrating how scientists collaborated to produce the main research results, identifying the leading research groups on Zika and the most influential researchers. Social Network Analysis was applied to the co-authorship networks formed from 2015 to 2016. The study showed that a Zika researcher’s influence is basically determined by three factors: (a) number of publications; (b) diversified partnerships; and (c) the links established with the research area’s pioneers.

Keywords:
Zika Virus; Authorship and Co-authorship in Scientific Publications; Social Networking; Cooperative Behavior

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 ¹1. Hayes EB. Zika virus outside Africa. Emerg Infect Dis 2009; 15:1347-50.. 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.

On Yap Island, approximately 73% of the inhabitants were infected and presented mild, short-lived symptoms, with many asymptomatic cases ²2. Lanciotti RS, Kosoy OL, Laven JJ, Velez JO, Lambert AJ, Johnson AJ, et al. Genetic and serologic properties of Zika virus associated with an epidemic, Yap State, Micronesia, 2007. Emerg Infect Dis 2008; 14:1232-9..

In late 2013, a new epidemic occurred in French Polynesia ³3. Musso D, Nilles EJ, Cao-Lormeau V-M. Rapid spread of emerging Zika virus in the Pacific area. Clin Microbiol Infect 2014; 20:O595-6., where the outbreak was larger, as shown by retrospective epidemiological studies that indicated the occurrence of approximately 30,000 infections and cases of Guillain-Barré syndrome associated with ZIKV infection, as well as reports of the first cases of perinatal transmission. Retrospective analysis of live births in this outbreak in French Polynesia identified, from March 2014 to May 2015, 17 cases of central nervous system malformations, including microcephaly in fetuses and newborns ⁴4. Lessler J, Chaisson LH, Kucirka LM, Bi Q, Grantz K, Salje H, et al. Assessing the global threat from Zika virus. Science 2016; 353:aaf8160..

According to Massad et al. ⁵5. Massad E, Burattini MN, Khan K, Struchiner CJ, Coutinho FAB, Wilder-Smith A. On the origin and timing of Zika virus introduction in Brazil. Epidemiol Infect 2017; 145:2303-12., the virus was introduced into Brazil between October 2013 and March 2014, coming from French Polynesia.

In the latter half of 2014, a new febrile illness was reported in the cities of Natal, state capital of Rio Grande do Norte, and Recife, state capital of Pernambuco. Following investigation of the cases, the circulation of ZIKV was also confirmed in the state of Bahia, in the city of Camaçari ⁶6. Luz KG, Santos GIV, Vieira RM. Zika virus fever. Epidemiol Serv Saúde 2015; 24:785-8.^,77. Campos GS, Bandeira AC, Sardi SI. Zika virus outbreak, Bahia, Brazil. Emerg Infect Dis 2015; 21:1885-6..

In 2015, the uncommon occurrence of microcephaly in newborns began to be detected in Pernambuco, with an unusual incidence rate. Studies on the association between Zika and microcephaly began in Brasil ⁸8. Oliveira CS, Vasconcelos PFC. Microcephaly and Zika virus. J Pediatr (Rio J.) 2016; 92:103-5.. However, the association was confirmed by the United States Centers for Disease Control and Prevention (CDC), which announced on April 13, 2016, the confirmation of the relationship between ZIKV and microcephaly in infants of mothers infected with the virus.

The emergency situation demanded speed and collective effort by researchers worldwide, and science hastened to investigate the disease and publish the results.

Publications are still the principal mechanism for the dissemination of scientific knowledge. Thus, research productivity and advances by universities and research institutes in state-of-the-art research are assessed by metrics directly related to the number of citations (e.g.: impact factor, i10-index, h-index, among others). Such metrics aim to estimate researchers’ reputation and academic productivity, as well the impact of their research, based on their publications ⁹9. Bollen J, van de Sompel H, Hagberg A, Chute R. A principal component analysis of 39 scientific impact measures. PLoS One 2009; 4:e6022.. However, these metrics have been criticized from various angles. Some criticisms emphasize the fact that impact measures based on these indicators overlook the more subtle and informal aspects of academic influence, such as community engagement, participation in research groups, and dissemination beyond the scientific community ¹⁰10. PLoS Medicine Editors. The impact factor game. PLoS Med 2006; 3:e291..

As important as analyzing researchers’ output is to analyze their engagement in the scientific community, their role in the creation and dissemination of knowledge, and the ways that groups in a given field of science evolve ¹¹11. Albuquerque RP, Oliveira J, Faria FF, Monclar R, Souza JM. Studying group dynamics through social networks analysis in a medical community. Soc Netw 2014; 3:134-41.^,1212. Morel CM, Serruya SJ, Penna GO, Guimarães R. Co-authorship network analysis: a powerful tool for strategic planning of research, development and capacity building programs on neglected diseases. PLoS Negl Trop Dis 2009; 3:e501.. In particular, metrics from Social Network Analysis (SNA) can be used to explore the relationships in networks of scientific collaboration, also known as Scientific Social Networks (SSN) ¹¹11. Albuquerque RP, Oliveira J, Faria FF, Monclar R, Souza JM. Studying group dynamics through social networks analysis in a medical community. Soc Netw 2014; 3:134-41.^,1313. Maia LFMP, Oliveira J. Investigation of research impacts on the Zika virus. An approach focusing on social network analysis and altmetrics. In: WebMedia '17. Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. Gramado: ACM Publications; 2017. p. 413-6.. In studies focused on co-authorship, this relationship can be measured as the proportion with which the same groups of authors publish articles in common, where such publications can be used to measure the strength of links between researchers. Co-authorship networks and most social structures are usually represented by graph structures. SNA consists of applying a set of metrics and algorithms to analyze the existing relations in these graph structures ¹⁴14. Barabási A-L, Pósfai M. Network science. Cambridge: Cambridge University Press; 2016..

This study aims to map and analyze SSN on Zika research, revealing how scientists collaborated to produce the leading results. The study addressed the following questions: “Who are the most influential researchers in terms of activity and collaboration in studies on Zika?”, and “What are the leading collaborative groups?”. Using SNA metrics together with productivity metrics, the goal is to better understand the representativeness and recognition of Zika researchers to present to the scientific community the most outstanding names in studies related to the disease at present.

Related studies

One pioneering study on scientific collaboration networks was by Newman ¹⁵15. Newman ME. The structure of scientific collaboration networks. Proc Natl Acad Sci U S A 2001; 98:404-9., 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 ¹⁶16. Freeman LC. Centrality in social networks conceptual clarification. Soc Netw 1978; 1:215-39. 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 ¹⁷17. Yan E, Ding Y. Applying centrality measures to impact analysis: a coauthorship network analysis. J Assoc Inf Sci Technol 2009; 60:2107-18. proposed a method to calculate researchers’ influence in SSN, applying the centralization metrics as proposed by Freeman ¹⁶16. Freeman LC. Centrality in social networks conceptual clarification. Soc Netw 1978; 1:215-39. together with PageRank ¹⁸18. Page L. Method for node ranking in a linked database. United States patent US 6,285,999. 2001 sep. 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. ¹⁹19. Liu X, Bollen J, Nelson ML, van de Sompel H. Co-authorship networks in the digital library research community. Inf Process Manag 2005; 41:1462-80. 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 ¹⁷17. Yan E, Ding Y. Applying centrality measures to impact analysis: a coauthorship network analysis. J Assoc Inf Sci Technol 2009; 60:2107-18. and Liu et al. ¹⁹19. Liu X, Bollen J, Nelson ML, van de Sompel H. Co-authorship networks in the digital library research community. Inf Process Manag 2005; 41:1462-80., 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. ¹²12. Morel CM, Serruya SJ, Penna GO, Guimarães R. Co-authorship network analysis: a powerful tool for strategic planning of research, development and capacity building programs on neglected diseases. PLoS Negl Trop Dis 2009; 3:e501. 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. ¹²12. Morel CM, Serruya SJ, Penna GO, Guimarães R. Co-authorship network analysis: a powerful tool for strategic planning of research, development and capacity building programs on neglected diseases. PLoS Negl Trop Dis 2009; 3:e501. 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. ¹¹11. Albuquerque RP, Oliveira J, Faria FF, Monclar R, Souza JM. Studying group dynamics through social networks analysis in a medical community. Soc Netw 2014; 3:134-41. 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 analyses 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 ²⁰20. Wasserman S, Faust K. Social network analysis: methods and applications. v. 8. Cambridge: Cambridge University Press; 1994. and centrality ¹⁶16. Freeman LC. Centrality in social networks conceptual clarification. Soc Netw 1978; 1:215-39. 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 ¹⁸18. Page L. Method for node ranking in a linked database. United States patent US 6,285,999. 2001 sep., 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 ²⁰20. Wasserman S, Faust K. Social network analysis: methods and applications. v. 8. Cambridge: Cambridge University Press; 1994.. 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 ¹³13. Maia LFMP, Oliveira J. Investigation of research impacts on the Zika virus. An approach focusing on social network analysis and altmetrics. In: WebMedia '17. Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. Gramado: ACM Publications; 2017. p. 413-6..

The process used (Figure 1), adapted from Maia & Oliveira ¹³13. Maia LFMP, Oliveira J. Investigation of research impacts on the Zika virus. An approach focusing on social network analysis and altmetrics. In: WebMedia '17. Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. Gramado: ACM Publications; 2017. p. 413-6. and Maia & Yagui ²¹21. Maia LFMP, Yagui MMM. Triplificação de dados de notícias sobre a Zika. In: Proceedings of the XIII Brazilian Symposium on Information Systems. Lavras: Sociedade Brasileira de Computação; 2017. p. 40-7., 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 ¹⁵15. Newman ME. The structure of scientific collaboration networks. Proc Natl Acad Sci U S A 2001; 98:404-9. 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 ²²22. Martins MFM. Análise bibliométrica de artigos científicos sobre o vírus Zika. RECIIS (Online) 2016; 10(1). https://www.reciis.icict.fiocruz.br/index.php/reciis/article/view/1096.
https://www.reciis.icict.fiocruz.br/inde... . 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 diameters) 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 ¹³13. Maia LFMP, Oliveira J. Investigation of research impacts on the Zika virus. An approach focusing on social network analysis and altmetrics. In: WebMedia '17. Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. Gramado: ACM Publications; 2017. p. 413-6., which incorporated the concepts of centrality applied to the work by Freeman ¹⁶16. Freeman LC. Centrality in social networks conceptual clarification. Soc Netw 1978; 1:215-39., Yan & Ding ¹⁷17. Yan E, Ding Y. Applying centrality measures to impact analysis: a coauthorship network analysis. J Assoc Inf Sci Technol 2009; 60:2107-18., and Liu et al. ¹⁹19. Liu X, Bollen J, Nelson ML, van de Sompel H. Co-authorship networks in the digital library research community. Inf Process Manag 2005; 41:1462-80.. 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.

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 ²³23. Duffy MR, Chen T-H, Hancock WT, Powers AM, Kool JL, Lanciotti RS, et al. Zika virus outbreak on Yap Island, Federated States of Micronesia. N Engl J Med 2009; 360:2536-43..

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.

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 co-authorship network with Silvia Sardi (SARDI SI) (UFBA), Antonio C. Bandeira (BANDEIRA AC) (Santa Helena Hospital, Camaçari/Bahia), Guilherme Ribeiro, and other researchers from IGM, in addition to partnerships with Charles Y. Chiu and Samia Naccache (2 publications) from the University of California (United States).

Another group linked by Brazilians includes Rubens Belfort Jr. (BELFORT R JR) and Maurício Maia (MAIA M), 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) 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).

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, 2015²⁴24. Microcephaly Epidemic Research Group. Microcephaly in infants, Pernambuco State, Brazil, 2015. Emerg Infect Dis 2016; 22:1090-3., 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.

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” ²⁵25. Centers for Disease Control and Prevention. CDC concludes Zika causes microcephaly and other birth defects. https://www.cdc.gov/media/releases/2016/s0413-zika-microcephaly.html (acessado em 14/Nov/2016).
https://www.cdc.gov/media/releases/2016/... .

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 state-of-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.

The study of the Zika epidemic in Puerto Rico appears in this network, again demonstrating the formation of subnetworks through geopolitical and institutional relations.

The co-authorship groups are quite interchangeable. Based on the most influential first authors, some co-authorships can be identified:

Compared to subnetwork 1, this cluster is denser and shows higher correlation between the four centrality metrics analyzed.

Subnetwork 3

This cluster consists mainly of authors affiliated with Institut Pasteur (Paris/France), Institut Pasteur (French Guiana), ILM (Tahiti), Aix-Marseille Université - AMU (Marseille), NRCA-IRBA (Marseille) and École de Pharmacie Genève-Lausanne, Université de Genève (Geneva/Switzerland).

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 ²⁷27. Cao-Lormeau V-M, Roche C, Teissier A, Robin E, Berry A-L, Mallet H-P, et al. Zika virus, French polynesia, South pacific, 2013. Emerg Infect Dis 2014; 20:1085-6..

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.

Leparc-Goffart has strong links in co-authorships with Xavier de Lamballerie (DE LAMBALLERIE X) from AMU (Marseille) and Marianne Maquart (MAQUART M) from NRCA-IRBA. Dominique Rousset (ROUSSET D) from Institut Pasteur (French Guiana) also appears as a coauthor linked to Leparc-Goffart, but with a weaker link.

In this subnetwork, Marianne Besnard (BESNARD M) (Hôpital Du Taaone, Tahiti) and Catherine Garel (GAREL C) (Hôpital Armand Trousseau, Assistance Publique-Hôpitaux de Paris, Université Pierre et Marie Curie, Paris-France) are important nodes that link the group of Leparc-Gofart to the group of Didier Musso and Cao-Lormeau.

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 ²²22. Martins MFM. Análise bibliométrica de artigos científicos sobre o vírus Zika. RECIIS (Online) 2016; 10(1). https://www.reciis.icict.fiocruz.br/index.php/reciis/article/view/1096.
https://www.reciis.icict.fiocruz.br/inde... .

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 ²⁸28. Maia LFMP, Oliveira J, Rabello ET, Lenzi M, Camargo Jr. KR. Scientific collaborations in Zika: identifying the main research groups through Social Scientific Network analysis. In: Proceedings of the International Symposium on Zika Virus Research. Marseille: ZIKAlliance; 2018. p. 101..

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 (altmetrics¹³13. Maia LFMP, Oliveira J. Investigation of research impacts on the Zika virus. An approach focusing on social network analysis and altmetrics. In: WebMedia '17. Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. Gramado: ACM Publications; 2017. p. 413-6.).

Acknowledgments

The authors wish to thank Prof. Kenneth Camargo Jr. (State University of Rio de Janeiro), Brazilian Graduate Studies Coordination Board (Capes), Brazilian National Research Council (CNPq), Rio de Janeiro State Research Foundation (FAPERJ), the Zika Social Sciences network (Fiocruz), and ZIKAlliance. The study was partially financed by the European Union’s Horizon 2020 Research and Innovation Programme, ZIKAlliance Grant Agreement n. 734548, and CNPq.

References

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