The Identification of Scientific Communities and Their Approach to Worldwide Malaria Research

International Journal of Environmental Research and Public Health Article The Identification of Scientific Communities and Their Approach to Worldwide Malaria Research JoséAntonio Garrido-Cardenas 1, Francisco Manzano-Agugliaro 2,* , Lilia González-Cerón3, Francisco Gil-Montoya 2, Alfredo Alcayde-Garcia 2, Nuria Novas 2 and Concepción Mesa-Valle 1 1Department of Biology and Geology, University of Almeria, 04120 Almeria, Spain; [email protected] (J.A.G.-C.); [email protected] (C.M.-V.) 2Department of Engineering, University of Almeria, CeiA3. 04120 Almeria, Spain; [email protected] (F.G.-M.); [email protected] (A.A.-G.); [email protected] (N.N.) 3Regional Center for Public Health Research, National Institute of Public Health, Tapachula 30700, Chiapas, Mexico; [email protected] *Correspondence: [email protected]; Tel.: +34-9500-15693 Received: 4 November 2018; Accepted: 28 November 2018; Published: 30 November 2018   Abstract: It is essential to establish a pattern to detect the strengths and weaknesses of working groups publishing on malaria, to promote coordination to facilitate the eradication of the disease. Given the complexity of the scientific network of groups and institutions studying malaria, it is necessary to use a mathematical algorithm that allows us to know the real structure of research on the disease in the world. In this work, articles with the word “malaria” in the title or author keywords gathered from Elsevier Scopus database were analyzed. By means of specific software, graphs were created. The analysis of the data allowed established different scientific communities, among which two were very diverse: one formed by those groups concerned about the vector transmission and control, and another one focused on the drug resistance of the parasite. Basic, applied, and operational research to eradicate malaria is an ambitious goal of the international institutions and the scientific community. The combination of effort and the establishment of a worldwide-scientific network that allows an effective interconnection (exchange) of knowledge, infrastructure technology, collaborators, financial resources, and datasets will contribute more effectively to end the disease. Keywords: malaria; vector; drug resistance; scientific community 1. Introduction Malaria is one of the infectious diseases that generates the greatest concern to global health [ 1 , 2 ]. According to the last report of the World Health Organization, in 2016, malaria produced almost half a million deaths and more than 200 million cases were estimated, with 90% of these cases occurring in African countries. Malaria is a mosquito-borne disease caused by a parasite of the genus Plasmodium, with six species that affect humans: Plasmodium falciparum, which causes the most deaths, Plasmodium vivax, the most widely spread except in sub-Saharan Africa, Plasmodium malariae,Plasmodium knowlesi, Plasmosdium ovale curtisi, and Plasmodium ovale wallikeri [3–6]. Plasmodium has a complex life cycle, developing partially in mosquitoes of the genus Anopheles [ 7 ]. In humans, the parasite has a multiplicative exoerythrocytic stage, in hepatic cells, and another intraerythrocytic phase. The beginning of the cycle occurs with the bite of an infected female Anopheles mosquito, which inoculates the sporozoites in the vertebrate host. These migrate to hepatic cells, where they multiply and transform into exoerythrocytic merozoites that invade red blood cells again, generating erythrocytic merozoites that can invade new red blood cells and generate a new batch of Int. J. Environ. Res. Public Health 2018,15, 2703; doi:10.3390/ijerph15122703 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2018,15, 2703 2 of 14 merozoites or developed into gametocytes. Gametocytes will be ingested when Anopheles females bite the host to feed on their blood. In P. vivax and P. ovale, the hepatic schizont can remain dormant harboring hypnozoites that can be reactivated years later. The malaria parasite is also genetically complex [ 8 ]. However, the current molecular tools make the analysis of genetic material available to produce data of great interest. The search for genes associated with drug resistance or phenotypes related to clinical manifestations of high pathogenicity, are two fundamental aspects addressed at a molecular level in malaria research. The nuclear genome of P. falciparum was sequenced in 2002 [ 6 ], and, later, the complete genomes of the rest of the human Plasmodium were obtained. These genomes have a size ranged from 23.3 to 33.6 Mb, organized into 14 chromosomes with a number of genes ranging from 5507 in P. falciparum to 7132 in P. ovale curtisi. In addition, Plasmodium parasites contain two more genomes. A mitochondrial genome, with the genes Cox1, Cox3 and Cytb [ 9 ], and an apicoplast genome, with genes related to the synthesis of fatty acids [10]. One of the most ambitious goals in current research on malaria is the search for a vaccine against the disease [ 11 ]. But, as already mentioned, the complex biology, genome, and life cycle of the parasite, as well as its ability to evade the protective immune responses of the host, have made the search unsuccessful. The design of malaria vaccines is very broad and varied. Currently, researchers are working with vaccines that act at different points in the Plasmodium life cycle. On the other hand, vaccines are being developed against a specific antigen, as well as vaccines against several antigens at different stages of the parasite lifecycle (multi-state vaccines), or even through the use of complete sporozoites. The use of viral vectors in heterologous prime-boost regimens represent another interesting alternative for the development of an effective vaccine. They are all the next-generation of malaria vaccines [ 12 , 13 ]. Other strategies being addressed in the fight against malaria focus on the use of mosquito nets and repellents to try to avoid mosquito bites [14]. The analysis of malaria research is an excessively complex system to carry out with a simple methodology. But, like other complex systems, it has a structure in scientific communities (SC), whose approach facilitates analysis [ 15 ]. The community detection is an issue that has been under study by the scientific community for a long time [ 16 ]. It is based on the search for substructures or communities that best represent the network’s topological features [ 17 ]. To obtain these substructures, it is common to use optimization techniques that combine the nodes in such a way that a certain objective function is maximized or minimized. In this regard, the modularity function has been successfully used by the scientific community to obtain a good definition of topologically related substructures. Modularity was proposed by Newman [ 18 ] and measures the quality of the cluster of nodes. The higher the value, the more precise the definition of the detected community structure. Since the invention of modularity, constant improvements have been generated [ 19 ] giving rise to one of the best-known algorithms: Girvan–Newman (GN), which is an algorithm based on a divisive method. This methodology has already been used successfully in many studies [ 20 , 21 ] and it is a very useful tool in bibliometric studies [ 22 , 23 ]. Thus, communities or clusters are generally groups of nodes that are more likely to be connected to each other than to members of other groups or to the rest of the network [ 24 ], in particular in bibliometric studies the scientific relations between groups of countries have been detected, or the topics around which a certain field of research is clustered [ 25 , 26 ]. Communities, in complex systems, can be defined as groups of nodes of a network, in which each node is densely connected with many other nodes in the network. The detection of communities is based on the principle that the nodes belonging to the same community are more likely to interact with each other. That is, they have greater connectivity. In this article, the authors introduce the novelty of using community detection to find SC associated with malaria research. This new approach will make it possible to establish future action policies in order to definitively eradicate malaria. Int. J. Environ. Res. Public Health 2018,15, 2703 3 of 14 2. Materials and Methods The two large scientific databases, Web of Science (WoS) and Scopus, raise the major issue of comparison and consistency of the statistics derived from different data sources [ 27 ]. Various research studies have assessed the superposition between both, concluding that they are similar when the coverage since 1996 is restricted (Scopus coverage) [ 28 , 29 ]. Regarding the journals reported in the two large scientific databases such as Web of Science (WoS) and Scopus, a comparative analysis shows that the range of journals in WoS (13,605 journals) is smaller than that of Scopus (20,346 journals) [ 30 ], and the relationships between the results obtained with both databases for the number of articles and the number of citations obtained by countries, as well as for their rankings, are extremely strong ( R2≈0.99 ) [ 31 ]. Therefore, only Scopus data have been used. This methodology has been used with success in different scientific fields [32–35]. A software has been programmed for the massive download of data from Scopus using the developer application that allows this database. The SW can be divided into four functional blocks as described below, see Figure 1: 1. Start Module. Which requires as input the desired search criteria, for example: TITLE (“malaria”) OR AUTHKEY (“malaria”) OR ABS (malaria), once the criteria is selected, the time range of the search is required and a list of SCOPUS apikeys needed to use the Scopus search service (https://dev.elsevier.com/). The output (data files in json format containing the same data as when searching the web, with the difference that one file is obtained for every 25 search results) is the input for the next module of the application. It is worth highlighting the large volume of data files generated for a search criterion such as the one mentioned above. 2. Data processing. In this second stage, each one of the results obtained in the previous phase are analyzed, obtaining as output: a single list of the Scopus-ID of authors and a list of the DOIs of the articles. 3. Data collection. In this stage, two threads of work are launched in parallel: one that oversees downloading all the information of the authors for each one of the Scopus-ID of the list, generating at least one file per author; and another that will download all the information of the articles for each one of the DOIs of the list of papers. Therefore, a directory for authors and another for articles where all the mentioned information will be stored is obtained as output. 4. Establishing relationships. This module, like the previous one, is computationally more expensive and is subdivided in two: one for the analysis of the authors and the other for the analysis of the articles. a. Authors collection. This submodule extracts all relevant information about the authors: H-Index, Name, Affiliation, Nation, etc. On the other hand, collaborations between authors are sought, for this purpose each author is examined, which are their articles, extracting all the authors from the papers of an author, establishing a bidirectional relationship between the author and the co-authors. b. Papers collection. In this thread we obtain information about the articles, Keys, authors, co-authors, references to other papers, etc. For each paper we obtain a unidirectional relationship with the paper that cites, thus generating the network of the graph. Int. J. Environ. Res. Public Health 2018,15, 2703 4 of 14 Int. J. Environ. Res. Public Health 2018, 15, x 4 of 14 Figure 1. Methodology flowchart The Elsevier Scopus database was used to obtain information about published works on malaria. A complete search was performed using the search query: (TITLE-ABS-AUTHKEY (malaria)). The search range used was from 1900 to 2017. It should be noted that a different search query can give different results. To facilitate the analysis of the data, a powerful network analysis tool, Gephi, was used. The data extraction was carried out automatically through the implementation of a specific software called Research Network Bot (ResNetBot) [36]. This software allows the elaboration of a graph in which each publication on malaria is represented by a node, and the connections between two nodes represent the existence of a citation of one article on the other. With these data, a new graphic is constructed. This time, the nodes represent the researchers, while the relationships between the nodes represent the collaborations in at least one publication. Data obtained through ResNetBot was refined with the OpenRefine software (OR) (formerly Google Refine) (Google, Mountain View, California, USA) and organized into spreadsheets to facilitate its management. The need to use OR is justified by the fact that authors often write slightly different keywords that identify the same concepts. In order to be able to unify these keywords OR is used because it provides the necessary mechanisms to find and merge written variations of the same word. E.g. “New York”, “New-York”, “new york”, etc. So, in our research the keywords need to be refined. 3. Results 3.1. Evolution of Scientific Output The search yielded 85,370 results, whose growth is represented in Figure 2. As can be observed, the data show two trends. The first of them extends throughout the twentieth century, and it is exponential, while the second one is from the year 2000 to the present, and it is linear. In the first of the periods, the adjustment to the trend line is optimal, giving rise to a regression coefficient R2 greater than 0.9. The only data that separates from this trend line corresponds to the number of articles published in the year 1946, which is 429. This data is abnormally high. In fact, it is not reached and exceeded completely until the 1980s. The explanation for this is found in two milestones of great importance in the fight against malaria that took place in that year. On the one hand, chloroquine was recognized and established as an effective and safe antimalarial agent [37]. And, on the other hand, the CDC (Communicable Disease Center) was created from the Office of MCMA (Malaria Control in War Areas), whose main objective was to fight against diseases such as malaria in the Figure 1. Methodology flowchart. The Elsevier Scopus database was used to obtain information about published works on malaria. A complete search was performed using the search query: (TITLE-ABS-AUTHKEY (malaria)). The search range used was from 1900 to 2017. It should be noted that a different search query can give different results. To facilitate the analysis of the data, a powerful network analysis tool, Gephi, was used. The data extraction was carried out automatically through the implementation of a specific software called Research Network Bot (ResNetBot) [ 36 ]. This software allows the elaboration of a graph in which each publication on malaria is represented by a node, and the connections between two nodes represent the existence of a citation of one article on the other. With these data, a new graphic is constructed. This time, the nodes represent the researchers, while the relationships between the nodes represent the collaborations in at least one publication. Data obtained through ResNetBot was refined with the OpenRefine software (OR) (formerly Google Refine) (Google, Mountain View, CA, USA) and organized into spreadsheets to facilitate its management. The need to use OR is justified by the fact that authors often write slightly different keywords that identify the same concepts. In order to be able to unify these keywords OR is used because it provides the necessary mechanisms to find and merge written variations of the same word. E.g., “New York”, “New-York”, “new york”, etc. So, in our research the keywords need to be refined. 3. Results 3.1. Evolution of Scientific Output The search yielded 85,370 results, whose growth is represented in Figure 2. As can be observed, the data show two trends. The first of them extends throughout the twentieth century, and it is exponential, while the second one is from the year 2000 to the present, and it is linear. In the first of the periods, the adjustment to the trend line is optimal, giving rise to a regression coefficient R 2 greater than 0.9. The only data that separates from this trend line corresponds to the number of articles published in the year 1946, which is 429. This data is abnormally high. In fact, it is not reached and exceeded completely until the 1980s. The explanation for this is found in two milestones of great importance in the fight against malaria that took place in that year. On the one hand, chloroquine was recognized and established as an effective and safe antimalarial agent [ 37 ]. And, on the other hand, Int. J. Environ. Res. Public Health 2018,15, 2703 5 of 14 the CDC (Communicable Disease Center) was created from the Office of MCMA (Malaria Control in War Areas), whose main objective was to fight against diseases such as malaria in the South of the United States during the Second World War. Currently, the CDC is the main North American public health agency and its mission is “collaborate to create the expertise, information, and tools that people, and communities need to protect their health—through health promotion, prevention of disease, injury, and disability—and preparedness for new health threats.” Although the initial objective of the CDC was the control and elimination of malaria, its current role is aimed at prevention and surveillance, because it is considered eradicated in the USA since 1951. Int. J. Environ. Res. Public Health 2018, 15, x 5 of 14 South of the United States during the Second World War. Currently, the CDC is the main North American public health agency and its mission is “collaborate to create the expertise, information, and tools that people, and communities need to protect their health—through health promotion, prevention of disease, injury, and disability—and preparedness for new health threats.” Although the initial objective of the CDC was the control and elimination of malaria, its current role is aimed at prevention and surveillance, because it is considered eradicated in the USA since 1951. Figure 2. Publications trends on Malaria from 1900–2017. The second period that can be observed in Figure 2 is adjusted to a linear trend line. Its slope is m = 159. That is, since 2000, each year, the average number of articles that have been published grows by more than 150 units. This highlights the enormous interest that research around malaria has, at present. 3.2. Authors and Countries in Malaria Research In the 85,370 published articles on malaria in the period studied, 148,876 authors and a total of 2,211,628 collaborations appear among them. In Table 1, the 20 most important authors can be observed. To establish this ranking, the eigenvector centrality or eigencentrality has been taken into account. In graph theory this is used to highlight the influence of a node in a network. Other values that could be used to measure the importance of an author are the H-index [38], the number of published articles, or the number of citations received by these articles. But the possibility that an author has worked in different areas or themes could mask the result. However, a high score of the eigenvector is directly related to the value that an author has within a network in which each author is identified with a node, and in which the connections of each node are preferably scored with other nodes of a high score. Figure 3-A shows the 455 authors represented by nodes with an eigencentrality value greater than 0.2, and their connections with the rest of the nodes (authors). Figure 3-B shows the 158 authors represented by nodes with an eigencentrality value greater than 0.4, and Figure 3-C shows the 31 authors represented by nodes with an eigencentrality value greater than 0.6. The very high density of relations that exist between authors, it is possible to emphasize above all with an eigenvector >0.2. The size of each node is proportional to the value of its eigencentrality, and the color is representative of the country from which the author´s institutional affiliation. Figure 2. Publications trends on Malaria from 1900–2017. The second period that can be observed in Figure 2is adjusted to a linear trend line. Its slope is m= 159. That is, since 2000, each year, the average number of articles that have been published grows by more than 150 units. This highlights the enormous interest that research around malaria has, at present. 3.2. Authors and Countries in Malaria Research In the 85,370 published articles on malaria in the period studied, 148,876 authors and a total of 2,211,628 collaborations appear among them. In Table 1, the 20 most important authors can be observed. To establish this ranking, the eigenvector centrality or eigencentrality has been taken into account. In graph theory this is used to highlight the influence of a node in a network. Other values that could be used to measure the importance of an author are the H-index [ 38 ], the number of published articles, or the number of citations received by these articles. But the possibility that an author has worked in different areas or themes could mask the result. However, a high score of the eigenvector is directly related to the value that an author has within a network in which each author is identified with a node, and in which the connections of each node are preferably scored with other nodes of a high score. Figure 3A shows the 455 authors represented by nodes with an eigencentrality value greater than 0.2, and their connections with the rest of the nodes (authors). Figure 3B shows the 158 authors represented by nodes with an eigencentrality value greater than 0.4, and Figure 3C shows the 31 authors represented by nodes with an eigencentrality value greater than 0.6. The very high density of relations that exist between authors, it is possible to emphasize above all with an eigenvector >0.2. The size of each node is proportional to the value of its eigencentrality, and the color is representative of the country from which the author’s institutional affiliation. Int. J. Environ. Res. Public Health 2018,15, 2703 6 of 14 Table 1. List of authors most frequent in malaria research articles. Author Eigencentrality NmNtH-Index Coauthor Cites Country Marsh K. 1.000000 392 468 84 1748 26032 Kenya Djimde A. 0.995692 120 123 33 1052 4656 Mali Drakeley C. 0.965505 284 294 58 1781 11934 United Kingdom Doumbo O. 0.957462 389 424 56 1921 13477 Mali Nosten F. 0.829049 512 568 78 2160 23142 Thailand Ouédraogo J. 0.816295 119 147 29 962 3137 Burkina Faso Borrmann S. 0.816208 87 87 31 676 3818 Kenya Mueller I. 0.808423 276 289 40 1224 5830 Australia Price R. 0.786288 227 241 54 1150 9966 United Kingdom Hien T. 0.786204 246 271 70 1763 19209 Viet Nam Plowe C. 0.749014 179 187 55 1216 10896 United States D’Alessandro U. 0.746263 298 315 43 1483 7497 United Kingdom White N. 0.726106 897 1146 114 3134 52466 Thailand Mayxay M. 0.703699 113 117 32 866 3941 Laos Bousema T. 0.702141 136 165 34 827 4205 United Kingdom Kwiatkowski D. 0.690173 270 335 63 2069 17684 United Kingdom Dondorp A. 0.685832 285 312 52 1367 10554 Netherlands Ogutu B. 0.681686 130 139 27 1017 3031 Kenya Fanello C. 0.680121 42 43 21 508 2648 Thailand Greenwood B. 0.676612 458 893 77 1848 23579 United Kingdom Nm, number of articles published on malaria; Nt, number of total articles published, including those whose main topic is not malaria; Coauthor, number of co-authors that appear in articles published on malaria; Cites, number of citations that have received articles published on malaria. Int. J. Environ. Res. Public Health 2018, 15, x 6 of 14 Table 1. List of authors most frequent in malaria research articles. Autho r Eigencentrality Nm Nt H-Index Coauthor Cites Country Marsh K. 1.000000 392 468 84 1748 26032 Kenya Djimde A. 0.995692 120 123 33 1052 4656 Mali Drakeley C. 0.965505 284 294 58 1781 11934 United Kingdom Doumbo O. 0.957462 389 424 56 1921 13477 Mali Nosten F. 0.829049 512 568 78 2160 23142 Thailand Ouédraogo J. 0.816295 119 147 29 962 3137 Burkina Faso Borrmann S. 0.816208 87 87 31 676 3818 Kenya Mueller I. 0.808423 276 289 40 1224 5830 Australia Price R. 0.786288 227 241 54 1150 9966 United Kingdom Hien T. 0.786204 246 271 70 1763 19209 Viet Nam Plowe C. 0.749014 179 187 55 1216 10896 United States D’Alessandro U. 0.746263 298 315 43 1483 7497 United Kingdom White N. 0.726106 897 1146 114 3134 52466 Thailand Mayxay M. 0.703699 113 117 32 866 3941 Laos Bousema T. 0.702141 136 165 34 827 4205 United Kingdom Kwiatkowski D. 0.690173 270 335 63 2069 17684 United Kingdom Dondorp A. 0.685832 285 312 52 1367 10554 Netherlands Ogutu B. 0.681686 130 139 27 1017 3031 Kenya Fanello C. 0.680121 42 43 21 508 2648 Thailand Greenwood B. 0.676612 458 893 77 1848 23579 United Kingdom Nm, number of articles published on malaria; Nt, number of total articles published, including those whose main topic is not malaria; Coauthor, number of co-authors that appear in articles published on malaria; Cites, number of citations that have received articles published on malaria. Figure 3 attempts to represent the relationship between authors from different countries, as well as their relevance. Therefore, a visualization (by color) of the authors from the different countries is sought, obtaining a general idea of which countries prevail over others, as well as the relevance of the main authors from one country versus authors from other countries. Due to the large number of nodes (authors) it is not possible to indicate their names. Figure 3. Cont. Int. J. Environ. Res. Public Health 2018,15, 2703 7 of 14 Int. J. Environ. Res. Public Health 2018, 15, x 7 of 14 Figure 3. Graph representing by nodes the nationality of the authors with an eigencentrality value and their connections with the rest of the nodes (authors). (A) Greater than 0.2; (B) Greater than 0.4; (C) Greater than 0.6. As can be observed in Table 1, the first 20 authors in malaria research belong to ten different countries. These can be considered in two groups. On the one hand, the African (Kenya, Mali and Burkina Faso) and Asians (Thailand, Viet Nam and Laos) countries, of great importance for the number of estimated cases and deaths produced by malaria; and, on the other hand, the European (United Kingdom, and Netherlands), American (USA) and Australian (Australia) countries, of great importance for the number of articles published. Figure 4 shows the countries with the highest number of cases estimated by the World Health Organization in the period 2010–2016 (Figure 4A), which have presented a greater number of deaths estimated in that same period (Figure 4B), and those that have published the most articles in 1900–2017 (Figure 4C). Burkina Faso and Mali are among the five countries with the highest number of deaths, while the United States and the United Kingdom are the two countries whose institutions publish the most articles on malaria. For this reason, it is not surprising that the main researchers working in these countries are those with the highest eigencentrality. Figure 3. Graph representing by nodes the nationality of the authors with an eigencentrality value and their connections with the rest of the nodes (authors). ( A ) Greater than 0.2; ( B ) Greater than 0.4; (C) Greater than 0.6. Figure 3attempts to represent the relationship between authors from different countries, as well as their relevance. Therefore, a visualization (by color) of the authors from the different countries is sought, obtaining a general idea of which countries prevail over others, as well as the relevance of the main authors from one country versus authors from other countries. Due to the large number of nodes (authors) it is not possible to indicate their names. As can be observed in Table 1, the first 20 authors in malaria research belong to ten different countries. These can be considered in two groups. On the one hand, the African (Kenya, Mali and Burkina Faso) and Asians (Thailand, Viet Nam and Laos) countries, of great importance for the number of estimated cases and deaths produced by malaria; and, on the other hand, the European (United Kingdom, and Netherlands), American (USA) and Australian (Australia) countries, of great importance for the number of articles published. Figure 4shows the countries with the highest number of cases estimated by the World Health Organization in the period 2010–2016 (Figure 4A), which have presented a greater number of deaths estimated in that same period (Figure 4B), and those that have published the most articles in 1900–2017 (Figure 4C). Burkina Faso and Mali are among the five countries with the highest number of deaths, while the United States and the United Kingdom are the two countries whose institutions publish the most articles on malaria. For this reason, it is not surprising that the main researchers working in these countries are those with the highest eigencentrality. Int. J. Environ. Res. Public Health 2018,15, 2703 8 of 14 Int. J. Environ. Res. Public Health 2018, 15, x 8 of 14 Figure 4. World map on malaria: (A) Cases estimated by the World Health Organization in the period 2010–2016; (B) Number of deaths estimated by the World Health Organization in the period 2010– 2016; (C) Number of publications on malaria in the period 1900–2017. 3.3. Communities Detection From 85,370 published articles, 714,979 citations were counted—the citations to nodes of topics other than malaria were not included. On the other hand, 149,143 keywords defined by the authors appear in these articles. Both data allow us, using genetic algorithms, to establish a series of thematic SC, of which, the most frequent are represented in Table 2. In the table only appear SC composed of at least 1% of the total of 85,370 articles published. To supplement Table 2, Figure 5 has been carried out. The information that codifies Figure 5 has to do with the relative size of one community in front of the other as well as the proximity of certain communities to each other, depending on how close the nodes in the graph are. Figure 4. World map on malaria: ( A ) Cases estimated by the World Health Organization in the period 2010–2016; ( B ) Number of deaths estimated by the World Health Organization in the period 2010–2016; (C) Number of publications on malaria in the period 1900–2017. 3.3. Communities Detection From 85,370 published articles, 714,979 citations were counted—the citations to nodes of topics other than malaria were not included. On the other hand, 149,143 keywords defined by the authors appear in these articles. Both data allow us, using genetic algorithms, to establish a series of thematic SC, of which, the most frequent are represented in Table 2. In the table only appear SC composed of at least 1% of the total of 85,370 articles published. To supplement Table 2, Figure 5has been carried out. The information that codifies Figure 5has to do with the relative size of one community in front of the other as well as the proximity of certain communities to each other, depending on how close the nodes in the graph are. Int. J. Environ. Res. Public Health 2018,15, 2703 9 of 14 Table 2. Main scientific communities in malaria research. Comm. % Community Topic Keyword 1 Keyword 2 Keyword 3 Keyword 4 Keyword 5 #1 5.64 Mosquitoes and insecticides An. gambiae Anopheles P. falciparum Malaria vector Vector control #2 5.40 Drug resistance P. falciparum Artemisinin Chloroquine Drug resistance Antimalarial #3 3.28 Apicomplexa P. falciparum Plasmodium P. berghei Apicoplast Erythrocyte #4 3.16 Severe malaria P. falciparum Cerebral malaria Cytokines Severe malaria Children #5 3.13 Malaria diagnosis P. falciparum Diagnosis Microscopy Plasmodium P. vivax #6 3.05 Vaccines P. falciparum Vaccine Plasmodium P. vivax Malaria vaccine #7 1.77 Pregnancy and VIH Pregnancy P. falciparum HIV Placenta Anemia #8 1.44 Plasmodium vivax P. vivax P. falciparum Primaquine Chloroquine Thrombocytopenia #9 1.35 Mosquitoes and immunity Mosquito P. falciparum Plasmodium An. gambiae Anopheles #10 1.31 Travel and drugs P. falciparum Mefloquine Chemoprophylaxis Travel Prophylaxis #11 1.03 Glucose-6-phosphate dehydrogenase P. falciparum G6PD deficiency Oxidative stress Thalassemia Sickle cell disease Comm., community; An. Anopheles;P. Plasmodium. %, percentage of articles belonging to a community with respect to the total number of published articles in malaria.