MARATTO

article · BMJ Global Health

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

2025Open accessNazi Boni University

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.

Research topics

  • Zoonotic diseases and public health
  • Data-Driven Disease Surveillance
  • scientometrics and bibliometrics research

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.1136/bmjgh-2024-017595

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.