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Score-Based Generative Data Assimilation for Integrating Aggregated Surveillance Data into Agent-Based Models in Epidemic Tracking — arXiv:2609.01434

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01434
License (as archived): CC0-1.0
Archived: 2026-10-17T07:06:11.131Z
SHA-256 of the archived text: a44b21f748e85f99b7ae5519c9a15bd56b5e9ea8a450b29564d9c992d24289ff
Stable URL of this page: https://metraipolis.com/documento/dc_93154757ffec

How to cite

Score-Based Generative Data Assimilation for Integrating Aggregated Surveillance Data into Agent-Based Models in Epidemic Tracking — arXiv:2609.01434 — arXiv (en), https://arxiv.org/abs/2609.01434 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_93154757ffec (Metraipolis archive dc_93154757ffec, sha256 a44b21f748e85f99b7ae5519c9a15bd56b5e9ea8a450b29564d9c992d24289ff)

BibTeX

@misc{dc93154757ffec,
  title = {Score-Based Generative Data Assimilation for Integrating Aggregated Surveillance Data into Agent-Based Models in Epidemic Tracking — arXiv:2609.01434},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01434},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_93154757ffec; sha256 a44b21f748e85f99b7ae5519c9a15bd56b5e9ea8a450b29564d9c992d24289ff},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01434. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Reliable epidemic monitoring often requires inferring regional infection burden and transmission heterogeneity from noisy, spatially aggregated, and potentially sparse surveillance data. Agent-based models (ABMs) are attractive for this task because they represent individual behavior, contact heterogeneity, and localized interventions, but these same features make them difficult to calibrate online. We develop a generative AI-based data-assimilation (GenDA) framework for partially observed epidemic ABMs that estimates both the epidemic state and a heterogeneous parameter field while respecting the gap between observable macrostates and latent agent-level microstates. GenDA combines a training-free, score-based generative update for macrostate correction with a direct parameter update based on macrostate discrepancies, followed by a macro-micro reassignment step that restores consistency with the ABM. In controlled and geographically explicit synthetic experiments, the framework recovers regional epidemic burden, dominant hotspot structures, and effective transmission heterogeneity from aggregated observations, while improving post-assimilation forecasts relative to state-only assimilation.

Tema degli esploratori: matematica e informatica teorica (retro-catalogo arXiv: annunci del 2026-09-02 in math.NA).
Categorie arXiv: Numerical Analysis (math.NA) ; Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2609.01434 — 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 2026-10-17T06:52:44.066Z dalla pagina https://arxiv.org/catchup/math.NA/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).