{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:19:18Z","timestamp":1781187558606,"version":"3.54.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"vor","delay-in-days":574,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201906210093"],"award-info":[{"award-number":["201906210093"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-20-14053"],"award-info":[{"award-number":["DMS-20-14053"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-18-07032"],"award-info":[{"award-number":["DMS-18-07032"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-20-14018"],"award-info":[{"award-number":["DMS-20-14018"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We propose a piecewise linear quantile trend model to analyse the trajectory of the COVID-19 daily new cases (i.e. the infection curve) simultaneously across multiple quantiles. The model is intuitive, interpretable and naturally captures the phase transitions of the epidemic growth rate via change-points. Unlike the mean trend model and least squares estimation, our quantile-based approach is robust to outliers, captures heteroscedasticity (commonly exhibited by COVID-19 infection curves) and automatically delivers both point and interval forecasts with minimal assumptions. Building on a self-normalized (SN) test statistic, this paper proposes a novel segmentation algorithm for multiple change-point estimation. Theoretical guarantees such as segmentation consistency are established under mild and verifiable assumptions. Using the proposed method, we analyse the COVID-19 infection curves in 35 major countries and discover patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. A simple change-adaptive two-stage forecasting scheme is further designed to generate short-term prediction of COVID-19 cumulative new cases and is shown to deliver accurate forecast valuable to public health decision-making.<\/jats:p>","DOI":"10.1111\/rssb.12453","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T17:15:24Z","timestamp":1628788524000},"page":"1589-1607","source":"Crossref","is-referenced-by-count":20,"title":["Modelling the COVID-19 Infection Trajectory: A Piecewise Linear Quantile Trend Model"],"prefix":"10.1093","volume":"84","author":[{"given":"Feiyu","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Statistics , , , Shanghai , China"},{"name":"School of Management , , , Shanghai , China"},{"name":"Fudan University , , , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zifeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Analytics, and Operations , , , Notre Dame , Indiana , USA"},{"name":"Mendoza College of Business , , , Notre Dame , Indiana , USA"},{"name":"University of Notre Dame , , , Notre Dame , Indiana , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Statistics , , Champaign , Illinois , USA"},{"name":"University of Illinois at Urbana-Champaign , , Champaign , Illinois , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"key":"2023031001055934900_","doi-asserted-by":"crossref","first-page":"e0230405","DOI":"10.1371\/journal.pone.0230405","article-title":"Data-based analysis, modelling and forecasting of the COVID-19 outbreak","volume":"15","author":"Anastassopoulou","year":"2020","journal-title":"PLoS 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