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Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs — arXiv:2610.21223

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21223
License (as archived): CC0-1.0
Archived: 2026-10-16T10:20:14.297Z
SHA-256 of the archived text: 4e8ee5ad8730915abc26ca26180248acf99cbfcca04c5381aad6d023af48bf4a
Stable URL of this page: https://metraipolis.com/documento/dc_f136e51e00a5

How to cite

Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs — arXiv:2610.21223 — arXiv (en), https://arxiv.org/abs/2610.21223 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f136e51e00a5 (Metraipolis archive dc_f136e51e00a5, sha256 4e8ee5ad8730915abc26ca26180248acf99cbfcca04c5381aad6d023af48bf4a)

BibTeX

@misc{dcf136e51e00a5,
  title = {Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs — arXiv:2610.21223},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21223},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f136e51e00a5; sha256 4e8ee5ad8730915abc26ca26180248acf99cbfcca04c5381aad6d023af48bf4a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21223. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate medium- to long-range epidemic forecasting requires extrapolating high-dimensional, heterogeneous and nonstationary dynamics from partial observations. Yet hybrid models often leave implicit which observations inform which epidemic processes and how learned graph interactions modify system state. We introduce LE-EpiGNODE, which replaces generic message passing with process-specific, mechanism-guided neural operators: age-contact information drives Lagrangian infection generation, whereas mobility drives conservative Eulerian population transport within a structure-preserving universal differential equation. We evaluated LE-EpiGNODE across 14 controlled synthetic mechanisms spanning time-modulated, state-dependent, nonlinear and coupled dynamics, three complementary COVID-19 settings, 21 comparator models and five targeted ablations. Forecasting gains tended to become more pronounced at longer horizons; at the longest horizon, LE-EpiGNODE achieved the lowest WMAPE in all three real-world datasets, reducing error by 17.7-40.0% relative to the best baseline. Controlled ablations linked these gains to the Lagrangian-Eulerian decomposition and process-aligned priors, while synthetic ground truth showed that learned operators and induced state fluxes track known generating dynamics. These results support mechanism-organized graph neural dynamics as a route to reliable long-horizon extrapolation with directly testable, process-level mechanistic interpretation beyond trajectory accuracy.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.21223 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:48:02.694Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).