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

Etienne Kouokam

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DOI: 10.1371/journal.pone.0344827
Published: 2026-06-18
Container: PLOS One
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0344827,
  title = {A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory},
  author = {Etienne Kouokam},
  year = {2026},
  journal = {PLOS One},
  doi = {10.1371/journal.pone.0344827},
  url = {https://doi.org/10.1371/journal.pone.0344827}
}

RIS

TY  - JOUR
TI  - A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory
AU  - Etienne Kouokam
PY  - 2026
JO  - PLOS One
DO  - 10.1371/journal.pone.0344827
UR  - https://doi.org/10.1371/journal.pone.0344827
ER  -

APA

Kouokam, E. (2026). A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory. PLOS One. https://doi.org/10.1371/journal.pone.0344827

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crossref · retrieved 2026-09-25T09:00:24.593Z