A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory

Etienne Kouokam

doi:10.1371/journal.pone.0344827

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Peer-reviewed

Research Article

A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory

Etienne Kouokam

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: etienne.kouokam@facsciences-uy1.cm

Affiliations Department of Computer Science, University of Yaoundé I, Cameroon, IRD-UMMISCO, Sorbonne Université, Paris, France

https://orcid.org/0000-0002-4064-0200

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A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory

Etienne Kouokam

Published: June 18, 2026
https://doi.org/10.1371/journal.pone.0344827

About the Authors

Etienne Kouokam

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: etienne.kouokam@facsciences-uy1.cm

Affiliations Department of Computer Science, University of Yaoundé I, Cameroon, IRD-UMMISCO, Sorbonne Université, Paris, France

https://orcid.org/0000-0002-4064-0200

Competing Interests

The authors have declared that no competing interests exist.

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Neural networks
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Network analysis
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Algebraic topology
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Machine learning
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Monte Carlo method
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Trees
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Infectious disease epidemiology
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