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Bayesian epidemic alignment for causal evaluation of seasonal infectious-disease interventions — arXiv:2608.16537

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.16537
License (as archived): CC0-1.0
Archived: 2026-10-18T20:04:34.355Z
SHA-256 of the archived text: b3c65b14a0920b4fc7a23c3994258e7d2815735ba1a4faad1ff613ff2017a73a
Stable URL of this page: https://metraipolis.com/documento/dc_16763530bd8a

How to cite

Bayesian epidemic alignment for causal evaluation of seasonal infectious-disease interventions — arXiv:2608.16537 — arXiv (en), https://arxiv.org/abs/2608.16537 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_16763530bd8a (Metraipolis archive dc_16763530bd8a, sha256 b3c65b14a0920b4fc7a23c3994258e7d2815735ba1a4faad1ff613ff2017a73a)

BibTeX

@misc{dc16763530bd8a,
  title = {Bayesian epidemic alignment for causal evaluation of seasonal infectious-disease interventions — arXiv:2608.16537},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.16537},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_16763530bd8a; sha256 b3c65b14a0920b4fc7a23c3994258e7d2815735ba1a4faad1ff613ff2017a73a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.16537. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Seasonal infectious-disease interventions are commonly evaluated with interrupted time-series or pre--post designs that align epidemics by calendar week. When epidemic onset, speed or peak timing differs between seasons, such comparisons confound a shift in epidemic phase with a change in disease burden. We propose a Bayesian causal count model in which season-specific affine transformations map calendar time to a latent epidemic clock, and intervention effects are estimated on that clock rather than on the calendar. The alignment is a model component rather than a preprocessing step, so uncertainty about epidemic timing propagates into every causal contrast. The model uses a negative-binomial observation distribution, hierarchical area, season and area-season effects, a shrunk Fourier epidemic curve, and a continuous programme-intensity exposure. Posterior g-computation yields prevented cases, prevented fractions, peak attenuation and epidemic displacement, under both a controlled contrast and a dynamic contrast that propagates disease history within each arm. A two-tier simulation study evaluates bias, root mean squared error, interval coverage and parameter recovery under stable timing, epidemic-clock variation, intensity-dependent ascertainment and area-level confounding. We illustrate the framework using open Catalan primary-care surveillance and respiratory syncytial virus immunisation data, with explicit attention to the overlap in programme intensity that identifies the effect.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-18 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2608.16537 — 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 2026-10-18T19:44:19.579Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).