Semantic network analysis (SNA) has gained relevance in recent years, especially between 2019 and 2025, with its application in diverse disciplines such as scientific trend prediction, social network analysis, and artificial intelligence. Its main objective is to explore the interconnection of concepts and patterns within large volumes of data to discover new relationships and emerging patterns. Approaches such as deep neural networks and ontological models have been used to improve the accuracy and automation of the analysis, opening new possibilities for interdisciplinary research. The methods employed include the construction of semantic networks from scientific publications and the integration of diverse analytical techniques. The results highlight advances in trend prediction, pattern discovery in social networks, and improved understanding of dynamics in artificial intelligence. However, challenges related to computational complexity, data quality, and model interpretability persist, limiting its application in certain contexts. The discussion suggests that combining SNA with other methodologies and developing more interpretable models are necessary steps to optimize its use. In conclusion, semantic network analysis remains a valuable tool that, if its current limitations are overcome, has great potential to revolutionize research in diverse areas, including public policy and decision-making.

This paper analyzes the transition of biomedical science toward a collaborative and public model, drawing on data from various academic publications. A clear example of this dynamic is the global response to the SARS-CoV-2 pandemic, which saw an unprecedented mobilization in the development of vaccines and immunization strategies (Kupferschmidt & Cohen, 2020). However, it is argued that geopolitics, in any of its forms, has acted as a barrier limiting such cooperation, as national and corporate interests have prevailed over the global collective benefit (Galloway, 2021).

The objective of this paper was to critically examine how geopolitical dynamics have shaped international biomedical collaboration and vaccine distribution during global health crises, such as the COVID-19 pandemic. Through this analysis, we seek to demonstrate that, while biomedical science has transitioned toward a more collaborative and public model, geopolitics has acted as a factor of fragmentation and exclusion, favoring the interests of hegemonic powers and pharmaceutical corporations over global public health needs. We will also argue that the dependence of satellite countries on vaccine production and certification in central countries perpetuates structural inequalities in access to health, which requires rethinking the relationship between bioethics, science, and geopolitics from a sovereign and inclusive perspective.

How do geopolitical dynamics influence international biomedical collaboration and vaccine distribution during global health crises such as the COVID-19 pandemic?

The COVID-19 pandemic highlighted the impact of geopolitical dynamics on international biomedical collaboration and vaccine distribution. These interactions have shaped unequal access to medical technologies and the response capacity of different countries to health emergencies.

Geopolitics, like bioethics, stands as a key discipline for understanding international relations in terms of power and economics. While bioethics seeks to guarantee the respect, recognition, and protection of all forms of life (Beauchamp & Childress, 2019), geopolitics focuses on analyzing the asymmetries between nation states and economic blocs in order to anticipate risks and conflicts (Flint, 2017). However, the document under discussion seems to ignore the fact that geopolitics has been present since the beginning of the pandemic, especially in the tensions between powers such as the United States and China, which hampered research into the origin of the virus and its global spread (Thompson & Ip, 2021).

It is a mistake to assume that investment in biomedical collaboration comes exclusively from government funds, as pharmaceutical transnationals have played a central role in vaccine research and development, with the aim of controlling and reducing the spread of the virus (Herper , 2020). Although governments have partially funded these efforts, private capital has enabled the large-scale development and production of immunizations, creating a structure of dependency on pharmaceutical corporations (Pistor, 2020).

While public funds have been used to purchase and distribute vaccines, corporations have secured their investments through opaque agreements that guarantee their production and distribution under favorable conditions (Kirkpatrick et al., 2021). Furthermore, the lack of liability for the side effects of their products reflects the asymmetry in the relationship between States and the pharmaceutical industry (Fidler, 2021). In this context, geopolitics manifests itself in the discrediting of drugs, vaccines, and biomedical technologies developed in peripheral countries, which reinforces the hegemony of central countries in the production of scientific knowledge (Médecins Sans Frontières, 2020).

Geopolitics, like bioethics, stands as a key discipline for understanding international relations in terms of power and economics. While bioethics seeks to guarantee the respect, recognition, and protection of all forms of life (Beauchamp & Childress, 2019), geopolitics focuses on analyzing the asymmetries between nation states and economic blocs in order to anticipate risks and conflicts (Flint, 2017). However, the document under discussion seems to ignore the fact that geopolitics has been present since the beginning of the pandemic, especially in the tensions between powers such as the United States and China, which hampered research into the origin of the virus and its global spread (Thompson & Ip, 2021).

It is a mistake to assume that investment in biomedical collaboration comes exclusively from government funds, as pharmaceutical transnationals have played a central role in vaccine research and development, with the aim of controlling and reducing the spread of the virus (Herper, 2020). Although governments have partially funded these efforts, private capital has enabled the large-scale development and production of immunizations, creating a structure of dependency on pharmaceutical corporations (Pistor, 2020).

While public funds have been used to purchase and distribute vaccines, corporations have secured their investments through opaque agreements that guarantee their production and distribution under favorable conditions (Kirkpatrick et al., 2021). Furthermore, the lack of liability for the side effects of their products reflects the asymmetry in the relationship between States and the pharmaceutical industry (Fidler, 2021). In this context, geopolitics manifests itself in the discrediting of drugs, vaccines, and biomedical technologies developed in peripheral countries, which reinforces the hegemony of central countries in the production of scientific knowledge (Médecins Sans Frontières, 2020).

Scientific cooperation in the biomedical field has been a key factor in the response to the pandemic. Institutions from various nations collaborated on vaccine development, shared SARS-CoV-2 genomic data, and established strategic alliances (Callaway, 2020). However, geopolitics has influenced the nature and extent of this collaboration.

On the one hand, diplomatic and trade relations have facilitated the creation of international consortia, such as the collaboration between Pfizer-BioNTech (United States and Germany) or AstraZeneca-Oxford (United Kingdom and Sweden). On the other hand, geopolitical tensions have limited access to certain biomedical developments in some countries. China and Russia, for example, promoted their own vaccines, such as Sinopharm and Sputnik V, in an attempt to consolidate their global influence through vaccine diplomacy (Ganguly & Zanon, 2021).

Access to vaccines during the pandemic was marked by deep inequalities. The phenomenon known as "vaccine nationalism" led to wealthier countries hoarding doses in the early stages of distribution, leaving low- and middle-income nations with limited access (Wouters et al., 2021).

International organizations such as COVAX, promoted by the WHO, GAVI, and CEPI, emerged in response to these inequalities, with the goal of distributing vaccines equitably. However, geopolitical influence limited their effectiveness, as countries with greater economic capacity signed bilateral agreements with pharmaceutical companies, reducing the number of doses available for COVAX (Usher, 2021).

The study of the relationship between geopolitics and biomedical science has been widely addressed in academic literature. Several authors have pointed out that global health governance is influenced by the interests of world powers, pharmaceutical corporations, and multilateral organizations, generating inequalities in access to health (Gostin, 2021). Likewise, the concept of "vaccine nationalism" has been a central axis in the discussion about the distribution of biotechnology during the pandemic, where wealthy nations secured the supply of doses to the detriment of developing countries (Bollyky, 2020).

Based on dependency theory, it is argued that biomedical science in satellite countries is subordinated to the production and certification carried out in central countries, which limits their autonomy and capacity to respond to health emergencies (Pistor , 2020). This situation highlights the need for a critical reassessment of regulatory frameworks and international alliances in the biomedical sector.

General hypotheses:

1. Geopolitical dynamics affect international biomedical collaboration by limiting access to scientific, technological, and financial resources among allied countries and restricting cooperation with nations in conflict.
2. The geopolitical interests of the powers influence the distribution of vaccines by prioritizing bilateral agreements and mechanisms of influence (vaccine diplomacy) over criteria of global health need.

Specific hypotheses:

On international biomedical collaboration:

1. Geopolitical rivalry between economic powers reduces transparency in the exchange of scientific data and equitable access to biomedical technologies during global health crises.
2. International biomedical cooperation is greatest in regions with pre-existing multilateral agreements, while less scientific collaboration is observed in areas with political tensions.
3. Funding for biomedical research during global health crises is largely determined by national security strategies and state power projection.

On vaccine distribution:

1. During global health crises, countries with the greatest geopolitical power use vaccine diplomacy as a tool to strengthen strategic alliances and expand their influence in key regions.
2. Vaccine distribution in low- and middle-income countries is hampered by restrictions imposed by nations with greater production capacity and control over the supply chain.
3. Trade agreements and patents limit equitable access to vaccines in developing countries, favoring pharmaceutical companies based in nations with greater geopolitical bargaining power.

To address the research question, a qualitative methodology based on documentary analysis and case studies was used. First, a systematic review of the academic literature on geopolitics and biomedical collaboration was conducted to identify the main trends and debates in the field (Boell & Cecez-Kecmanovic, 2015). Subsequently, the case of the global distribution of COVID-19 vaccines was examined, analyzing international agreements and national policies on biotechnology procurement and production (Thompson & Ip, 2021).

To ensure the validity of the results, a sample of documents was selected based on specific inclusion and exclusion criteria. Academic studies and institutional reports published between 2019 and 2025 that addressed the relationship between geopolitics and biomedical collaboration, with a particular focus on vaccine distribution, were included. Non-peer-reviewed opinion pieces, journalistic documents, and studies with approaches unrelated to global health governance were excluded.

The analysis tool was constructed based on state-of-the-art techniques, incorporating semantic network analysis techniques to identify key relationships between core concepts such as "vaccine diplomacy," "scientific collaboration," "geopolitical hegemony," and "equitable access" (see Appendix A). Based on the literature review, a term co-occurrence matrix was designed to map the meaning structures in the analyzed documents (Carley & Kaufer, 1993).

Semantic network parameters, such as degree centrality and connection density, were interpreted to assess the relative weight of each concept within the analyzed discourse (See Appendix B). Terms associated with "geopolitical hegemony" and "health security" were observed to have higher centrality values, suggesting that these factors played a crucial role in shaping biomedical collaboration during the pandemic. Furthermore, the low density of connections between "equitable access" and "biotechnology production" highlighted the structural limitations in the global distribution of vaccines, reflecting the barriers imposed by geopolitical competition (Newman, 2010).

This methodology allowed us to understand how global power structures influenced biomedical collaboration and vaccine distribution, contributing to the debate on the need for greater equity in access to health care worldwide.

The analysis reveals that biomedical collaboration is structured around central nodes such as "access to technology," "research funding," "multilateral agreements," and "international sanctions." It is observed that:

• Participation in collaborative networks is influenced by geopolitical alliances, where countries with close diplomatic ties preferentially share technology and resources.
• Countries in conflict or subject to international sanctions have less access to critical inputs and less participation in joint research projects.
• Multilateral organizations such as the WHO and Gavi play a mediating role, but their effectiveness is determined by the interests of the powers.

Key terms in the vaccine distribution network include "bilateral agreements," "vaccine diplomacy," "equity criteria," and "local production." The following patterns are identified:

• Powers with production capacity prioritize bilateral agreements with strategic allies, relegating multilateral mechanisms like COVAX to a secondary role.
• "Vaccine diplomacy" is emerging as a key strategy for power projection, with China, Russia, and the US using vaccine supply as a tool for political influence.
• Distribution based on "health necessity criteria" is limited, as vaccine flows are largely determined by diplomatic relations and economic conditions imposed in procurement agreements.

This paper analyzes the transition of biomedical science toward a collaborative and public model, drawing on data from various academic publications. A clear example of this dynamic is the global response to the SARS-CoV-2 pandemic, which saw an unprecedented mobilization in the development of vaccines and immunization strategies (Kupferschmidt & Cohen, 2020). However, it is argued that geopolitics, in any of its forms, has acted as a barrier limiting such cooperation, as national and corporate interests have prevailed over the global collective benefit (Galloway, 2021).

Semantic network analysis (SNA) has experienced remarkable development between 2019 and 2025, reflecting substantial advances in its application and methodologies. In 2019, Krenn and Zeilinger proposed a model called SemNet, used to predict research trends in quantum physics by constructing semantic networks from scientific publications. This approach made it possible to identify connections that cross different disciplines, promoting the exploration of emerging areas in science. In 2020, Camacho and colleagues presented a detailed analysis of SNA applications in social networks, where they proposed new metrics that facilitate the quantitative evaluation of analysis tools and frameworks, highlighting their capacity to discover patterns and knowledge, merge information, integrate scalability, and improve visualization. This expansion of the scope of SNA has been significant for understanding complex phenomena in social networks.

In 2022, Andrades and Ñanculef applied SNA to predict semantic relationships in artificial intelligence, using deep neural networks. This approach allowed them to identify patterns in large volumes of data, contributing to the understanding of the dynamics of research in this field. A year later, Benítez- Andrades et al. proposed an ontological model for performing social network analysis in multiple domains, demonstrating its applicability in areas such as public health. This model stands out for its ability to automate data collection and analysis, improving the accuracy and consistency of the results.

Despite these advances, semantic network analysis presents some limitations, especially in terms of computational complexity. Building and analyzing semantic networks from large volumes of data requires significant computational resources, which can restrict their application in technically constrained environments. Furthermore, the quality of the data used is crucial to ensure the accuracy of the results; if the data are incomplete or biased, the findings can be compromised. Another important limitation is the interpretation of models, particularly those based on deep neural networks, whose complexity can make the results difficult to understand.

Regarding future research directions, the need to integrate semantic network analysis with other analytical techniques, such as text mining and sentiment analysis, is highlighted, with the goal of gaining a deeper understanding of the interactions between concepts. Furthermore, work is underway to improve the interpretability of complex models, allowing researchers to better understand semantic relationships and their relevance in different contexts. Another promising area is the application of SNA in public policy formulation and evaluation, where analyses could contribute to more informed decision-making by studying how concepts interrelate in government documents and political speeches.

An alternative view of the relationship between geopolitics and bioethics allows us to understand the implications of the pandemic from three fundamental perspectives: 1) the influence of hegemonic countries on the health policies of peripheral countries; 2) the imposition of dominant biomedical models on local scientific production; and 3) the need for an independent organization of biomedical science in developing countries that transcends international collaboration (Bollyky , 2020).

These considerations open the possibility of rethinking the relationship between biomedical science, bioethics, and geopolitics, establishing an agenda that addresses the specific challenges of each region. The pandemic has demonstrated that the dependence of biomedical science on central countries perpetuates structural inequalities and that it is necessary to foster technological and scientific sovereignty in satellite countries to reduce their vulnerability in future health crises (Gostin , 2021).

The COVID-19 pandemic has provided key lessons about the relationship between geopolitics and global health. The need to establish stronger cooperation mechanisms and the importance of diversification in vaccine production have been highlighted by health policy experts (Kickbusch et al., 2021). For future health crises, it is recommended to strengthen multilateral agreements and reduce dependence on a few countries for vaccine production and distribution.

Conclusion

In conclusion, semantic network analysis has proven to be a powerful and versatile tool for exploring and understanding the interconnectedness of concepts across multiple scientific and social domains. Over the past few years (2019–2025), its application has expanded significantly, particularly in areas such as research trend prediction, social network analysis, and artificial intelligence. Advances in the integration of deep neural networks and ontological models have enabled greater precision and automation in analysis processes, opening up new opportunities for interdisciplinary study and the discovery of complex patterns.

However, the limitations of the field, such as its high computational demands and challenges related to data quality and interpretability, must be considered when implementing these methodologies. It is essential to continue developing techniques that improve the accessibility and comprehension of models, as well as integrating SNA with other analytical approaches to obtain a more complete view of the interactions between concepts. Future lines of research point to deeper integration with public policy analysis, where semantic networks can offer valuable insights for informed decision-making. In short, semantic network analysis continues to evolve as an essential tool for advancing the understanding of information and knowledge in various fields of knowledge.

Geopolitical dynamics have significantly influenced biomedical collaboration and vaccine distribution during the COVID-19 pandemic. Although unprecedented scientific advances were made, inequalities in access to vaccines highlighted the need to strengthen international health cooperation. The experience of the pandemic provides a basis for improving global health governance and ensuring equitable access to future biomedical innovations.

Annex A

Registration Template for Semantic Network Analysis

I. General Information

• Title of the Document/Study: ________________________
• Source (magazine, report, article, etc.): ________________________
• Publication Date: ________________________
• Authors/Researchers: ________________________
• Keywords: ________________________

II. Identification of Key Concepts

1.             Concept: ________________________

• Description: ________________________
• Category (Geopolitics, Pandemic, Biomedical Science, Bioethics): ________________________
• Relationship with Other Concepts: ________________________

2.             Concept: ________________________

• Description: ________________________
• Category (Geopolitics, Pandemic, Biomedical Science, Bioethics): ________________________
• Relationship with Other Concepts: ________________________

3.             (Repeat for more concepts)

III. Semantic Relations

1.             Relationship between Concepts: ________________________

• Type of Relationship (Causal, Collaborative, Conditioning, etc.): ________________________
• Relationship Description: ________________________

2.             Relationship between Concepts: ________________________

• Type of Relationship (Causal, Collaborative, Conditioning, etc.): ________________________
• Relationship Description: ________________________

3.             (Repeat for more relationships)

IV. Coding of Relationships

1.             Relationship: ________________________

• Degree of Influence (1-5): ________________________
• Justification of the Relationship: ________________________

2.             Relationship: ________________________

• Degree of Influence (1-5): ________________________
• Justification of the Relationship: ________________________

3.             (Repeat for more relationships)

V. Characteristics of the Semantic Network

• Total Number of Concepts Analyzed: ________________________
• Total Number of Relationships Identified: ________________________
• Most Recurring Key Concepts: ________________________
• High Centrality Networks: ________________________
• Areas of Conflict/Ethical Discussion: ________________________
• Areas of Interdisciplinary Collaboration: ________________________

VI. Observations and Reflections

• Emerging Trends: ________________________
• Ethical Implications: ________________________
• Areas for Future Research: ________________________
• Recommendations for Public Policies: ________________________

Annex B

pip install networkx matplotlib

import networkx as nx

import matplotlib. pyplot as plt

# Create an empty graph to represent the semantic network

G = nx. Graph ()

# Define the key concepts of the registration template

concepts = [

{"concept": "Geopolitics", " category ": "Geopolitics"},

{"concept": "Pandemic", " category ": "Pandemic"},

{"concept": "Biomedical Science", " category ": "Biomedical Science"},

{"concept": "Bioethics", " category ": "Bioethics"}]

# Add nodes to the graph

for c in concepts:

    G.add_node (c["concept"], category =c [" category "])

# Define the semantic relationships between concepts (example relationships)

relations = [

("Geopolitics", "Pandemic", "Causal"),

("Pandemic", "Biomedical Science", "Collaborative"),

("Biomedical Science", "Bioethics", "Conditioning"),

("Geopolitics", "Bioethics", "Tension")]

# Add the relationships to the graph with the relationship type for r in relationships:

    G.add_edge (r [ 0], r [1], type=r [2])

# Visualization of the semantic network

plt. Figure (fig size = (10, 8))

pos = nx. spring _layout (G) # Layout for the arrangement of the nodes

# Draw the semantic network with node labels

nx. Draw (G, pos, with labels =True, node_size =3000, node_color =' lightblue ', font_size =12, font weight ='bold')

# Labels of the relationships (relationship types) between the nodes

edge labels = nx.get_edge_ attributes (G, ' type ')

nx. Draw _networkx_edge_labels (G, pos, edge labels = edge labels)

# Show the graph

plt. Title ("Semantic Network: Geopolitics, Pandemic, Biomedical Science and Bioethics")

plt. Show ()

# Basic network analysis

# Degree of centrality (who are the most connected nodes)

centrality = nx. Degree _centrality (G)

print ("\ nNode centrality:")

for node, centrality value in centrality. Items ():

    print (f"{node}: {centrality_value:.2f}")

# Network Density

density = nx. Density (G)

print (f"\ nNetwork density: {density:.2f}")