{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:26:45Z","timestamp":1767176805659,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013709","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000}}],"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100018489","name":"Center for Machine Learning and Health, School of Computer Science, Carnegie Mellon University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018489","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000030","name":"Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["U01IP001121"],"award-info":[{"award-number":["U01IP001121"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Epidemic data streams undergo frequent revisions due to reporting delays (\u201cbackfill\u201d) and other factors. Relying on tentative surveillance values can seriously degrade the quality of situational awareness, forecasting accuracy and decision-making. We introduce Delphi Revision Forecast (Delphi-RF), a real-time data revision forecasting framework using nonparametric quantile regression, applicable to both counts and proportions (fractions) in public health reporting. By incorporating all available revisions up to a given estimation date, Delphi-RF models revision dynamics and generates distributional forecasts of finalized surveillance values. Applied to daily COVID-19 data (insurance claims, antigen tests, confirmed cases) and weekly dengue and influenza-like illness (ILI) case counts, Delphi-RF delivers accurate revision forecasts, particularly in early reporting stages. In addition, it improves computational efficiency by more than 10-100x compared to existing methods, making it a scalable solution for real-time public health surveillance.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013709","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:41:11Z","timestamp":1763664071000},"page":"e1013709","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-time forecasting of data revisions in epidemic surveillance streams"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1447-5018","authenticated-orcid":true,"given":"Jingjing","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron","family":"Rumack","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan","family":"Wilder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3274-5862","authenticated-orcid":true,"given":"Roni","family":"Rosenfeld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"issue":"4","key":"pcbi.1013709.ref001","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007735","article-title":"Nowcasting by Bayesian smoothing: a flexible, generalizable model for real-time epidemic tracking","volume":"16","author":"SF McGough","year":"2020","journal-title":"PLoS Comput Biol."},{"issue":"51","key":"pcbi.1013709.ref002","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2111456118","article-title":"Epidemic tracking and forecasting: lessons learned from a tumultuous year","volume":"118","author":"R Rosenfeld","year":"2021","journal-title":"Proc Natl Acad Sci U S A."},{"issue":"10","key":"pcbi.1013709.ref003","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1005964","article-title":"What to know before forecasting the flu","volume":"14","author":"P Chakraborty","year":"2018","journal-title":"PLoS Comput Biol."},{"issue":"11","key":"pcbi.1013709.ref004","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007518","article-title":"Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data","volume":"15","author":"P Rangarajan","year":"2019","journal-title":"PLoS Comput Biol."},{"issue":"17","key":"pcbi.1013709.ref005","doi-asserted-by":"crossref","first-page":"15393","DOI":"10.1609\/aaai.v35i17.17808","article-title":"DeepCOVID: an operational deep learning-driven framework for explainable real-time COVID-19 forecasting","volume":"35","author":"A Rodr\u00edguez","year":"2021","journal-title":"AAAI."},{"issue":"4","key":"pcbi.1013709.ref006","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s41745-020-00200-6","article-title":"Mathematical models for COVID-19 pandemic: a comparative analysis","volume":"100","author":"A Adiga","year":"2020","journal-title":"J Indian Inst Sci."},{"key":"pcbi.1013709.ref007","doi-asserted-by":"crossref","DOI":"10.1093\/acrefore\/9780190625979.013.248","volume-title":"Data revisions and real-time forecasting","author":"MP Clements","year":"2019"},{"issue":"8","key":"pcbi.1013709.ref008","doi-asserted-by":"crossref","first-page":"3146","DOI":"10.1073\/pnas.1812594116","article-title":"A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States","volume":"116","author":"NG Reich","year":"2019","journal-title":"Proc Natl Acad Sci U S A."},{"key":"pcbi.1013709.ref009","doi-asserted-by":"crossref","unstructured":"Chakraborty P, Khadivi P, Lewis B, Mahendiran A, Chen J, Butler P. Forecasting a moving target: ensemble models for ILI case count predictions. 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