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. 2025 May 16;10(5):e017595.
doi: 10.1136/bmjgh-2024-017595.

Clustering and visualisation of the GABRIEL network expertise in the field of infectious diseases

Cécile Chauvel  1   2 , Philippe Vanhems  2   3 , Marie-Charlotte Quemin  4 , Marianne Abifadel  5 , Shally Awasthi  6 , Sayera Banu  7 , Silvia Figueiredo Costa  8 , Sara Eyangoh  9 , Monzer Hamze  10 , Zakir Hossain  11 , Bourema Kouriba  12 , Daniel Mukadi-Bamuleka  13 , Francine Ntoumi  14 , Abdoul-Salam Ouedraogo  15 , Phimpha Paboriboune  16 , Jean William Pape  17 , Chan Leakhena Phoeung  18 , Firdausi Qadri  19 , Ana Tereza Ribeiro Vasconcelos  20 , Graciela Russomando  21 , Luc Samison  22 , Marilda Agudo Mendonça Siqueira  23 , Nestani Tukvadze  24 , Jianwei Wang  25 , Florence Komurian Pradel  26
Affiliations

Clustering and visualisation of the GABRIEL network expertise in the field of infectious diseases

Cécile Chauvel et al. BMJ Glob Health. .

Abstract

Introduction: The Global Approach to Biology Research, Infectious diseases and Epidemics in Low-income countries (GABRIEL) network is an international scientific network of 21 centres coordinated by the Merieux Foundation (Lyon, France). Mapping and characterising the similarities and differences in expertise and activities across four major infectious diseases (tuberculosis, antimicrobial-resistant infections, acute respiratory infections and emerging pathogens) among these centres would help to provide a better understanding of the network's capacity. It will also highlight how the applied methodology can enhance information sharing within research networks.

Methods: Each centre responded to a questionnaire on their core activities and research themes. An advanced multivariate analysis was performed to relate all items together and highlight new synergies among members of the GABRIEL network. Similarities were found using a clustering algorithm and data were visualised using alluvial plots.

Results: This strategy enabled to find new patterns in the GABRIEL network for the implementation of new projects on global health, regardless of geographical proximity or historical connections. Five clusters based on core activities, consisting of 6, 1, 3, 9 and 2 research units, respectively, have been identified, with clusters 1 and 4, including the majority of the units. Four clusters have been defined based on the four major infectious diseases, comprising 7, 3, 5 and 6 research units, respectively.

Conclusions: The same methodology could also be applied to identify proximities on other networks of experts or between members of different networks for more efficient research or surveillance global programmes.

Keywords: Global Health; Infections, diseases, disorders, injuries.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1. Map of the GABRIEL members. GABRIEL, Global Approach to Biology Research, Infectious diseases and Epidemics in Low-income countries.
Figure 2
Figure 2. Clustering on core activities (A) and research themes (B). Plots of positive responses to each feature for each research institution were coloured according to cluster membership. A dot symbolises that the research institution in the column performs the type of core activity in row (A) or has activities related to the research theme in row (B). AMR, antimicrobial resistance; ARI, acute respiratory infection, including COVID-19.
Figure 3
Figure 3. Alluvial plot illustrating the distribution of 21 research units, colour-coded by cluster membership across different: (A) core activities (reference or focal point; training; diagnostic services; research; passive and active surveillance) and (B) research themes (tuberculosis; AMR; emerging pathogens; other pathogens; ARI). AMR, antimicrobial resistance; ARI: acute respiratory infection, including COVID-19.
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