{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:57:09Z","timestamp":1791464229084,"version":"4.3.4"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Science Technology Agency","award":["JPMJBY24H6"],"award-info":[{"award-number":["JPMJBY24H6"]}]},{"DOI":"10.13039\/501100000646","name":"JSPS","doi-asserted-by":"publisher","award":["26K02664"],"award-info":[{"award-number":["26K02664"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate estimation of incubation period is fundamental to surveillance for infectious diseases. This task faces two major challenges: (1) surveillance data are often coarsely observed, comprising interval- and right-censored observations, and (2) patient-level data are frequently fragmented across multiple facilities and cannot be pooled due to privacy constraints. While traditional meta-analysis (MA) aggregates summary statistics, its one-shot nature limits the ability to fit complex hierarchical models. We propose a novel federated Bayesian framework, Federated Stochastic Variational Inference (Fed-SVI), which extends the summary-statistic philosophy of MA into an iterative, model-based federated approach. Our method fits a hierarchical lognormal model, explicitly modeling heterogeneity while estimating a global distribution. To fit this model privately, Fed-SVI iteratively exchanges summary statistics (local variational parameters). Each facility analytically computes these local parameters, rigorously incorporating the likelihood contributions of coarsely-observed data via expectations of the truncated normal distribution. A central server aggregates these parameters using stochastic natural-gradient ascent, enabling efficient information borrowing and mini-batch updates. Through extensive simulations, we demonstrate that Fed-SVI achieves accuracy nearly identical to a fully centralized analysis, while substantially outperforming naive federated or MA methods that discard censored information. We apply Fed-SVI to a distributed COVID-19 line list from 177 facilities, yielding results consistent with centralized estimates. Fed-SVI provides a scalable, accurate, and privacy-preserving solution for robust surveillance in modern data networks.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag172","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:27:04Z","timestamp":1789756024000},"source":"Crossref","is-referenced-by-count":0,"title":["Meta-analytical federated surveillance for incubation period with interval- and right-censored data with stochastic variational inference"],"prefix":"10.1093","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3525-5092","authenticated-orcid":false,"given":"Daisuke","family":"Yoneoka","sequence":"first","affiliation":[{"name":"National Institute of Infectious Diseases, Japan Institute for Health Security , Tokyo, 1628640 ,","place":["Japan"]},{"name":"Graduate School of Medicine, The University of Tokyo , Tokyo, 1130033 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