{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T18:34:23Z","timestamp":1774290863888,"version":"3.50.1"},"reference-count":33,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72033002"],"award-info":[{"award-number":["72033002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SUSTech-NUS Joint Research Program"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Timely detection of infectious disease outbreaks is critical for effective public health response. The effective reproduction number (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>t<\/jats:italic>\n                    <\/jats:sub>\n                    ) is a key metric that captures transmission dynamics and signals the potential onset of outbreaks when it rises above 1. However, day-of-the-week and public holiday effects, along with random fluctuations in reported cases, can distort\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>t<\/jats:italic>\n                    <\/jats:sub>\n                    estimates and reduce their usefulness for real-time surveillance. In this study, we present an\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>t<\/jats:italic>\n                    <\/jats:sub>\n                    -based outbreak detection framework that integrates calendar-aware smoothing with bootstrap inference to quantify the uncertainty of smoothed\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>t<\/jats:italic>\n                    <\/jats:sub>\n                    estimates. Using daily COVID-19 case data from Singapore, we evaluate several smoothing approaches\u2014including a working-day moving average (MAH) that adjusts for public holidays\u2014and compare the performance of the proposed method with established outbreak detection algorithms such as Early Aberration Reporting System (EARS), Bayesian-based detection methods (EpiEstim) and logistic regression\u2013based approaches. In our framework, calendar-aware smoothing is not a generic pre-processing choice but a necessary, model-agnostic step that produces\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>t<\/jats:italic>\n                    <\/jats:sub>\n                    inputs with reduced calendar artefacts. This makes subsequent inference and testing on\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>t<\/jats:italic>\n                    <\/jats:sub>\n                    both more stable and more interpretable. Our results show that smoothing, particularly with MAH, improves the stability of\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>t<\/jats:italic>\n                    <\/jats:sub>\n                    estimates and enables more reliable outbreak detection. The proposed method consistently demonstrates superior timeliness across observed and simulated outbreaks, while maintaining desired false positive rates. Simulation studies further confirm its robustness under varying sample sizes and case volumes, highlighting advantages over other methods. In conclusion, the proposed method offers a simple, interpretable, and theoretically grounded framework for early outbreak detection. Its consistent performance across real and simulated data suggests it may be broadly applicable to other infectious diseases with similar transmission dynamics.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0345088","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T17:47:10Z","timestamp":1774288030000},"page":"e0345088","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A smoothing and bootstrap-based framework for early outbreak detection"],"prefix":"10.1371","volume":"21","author":[{"given":"Lengyang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingcun","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ee Hui","family":"Goh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"pone.0345088.ref001","doi-asserted-by":"crossref","DOI":"10.1017\/S0950268820001314","article-title":"The COVID-19 pandemic: a new challenge for syndromic surveillance","volume":"148","author":"AJ Elliot","year":"2020","journal-title":"Epidemiol Infect"},{"issue":"2","key":"pone.0345088.ref002","first-page":"410","article-title":"Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic","volume":"10","author":"W-H Hu","year":"2024","journal-title":"Infect Dis Model"},{"issue":"3","key":"pone.0345088.ref003","doi-asserted-by":"crossref","DOI":"10.5210\/ojphi.v3i3.3798","article-title":"Probabilistic, Decision-theoretic Disease Surveillance and Control","volume":"3","author":"M Wagner","year":"2011","journal-title":"Online J Public Health Inform"},{"issue":"1","key":"pone.0345088.ref004","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1177\/096228029300200103","article-title":"The estimation of the basic reproduction number for infectious diseases","volume":"2","author":"K Dietz","year":"1993","journal-title":"Stat Methods Med Res"},{"issue":"2","key":"pone.0345088.ref005","article-title":"Covid-19 \u2014 Navigating the Uncharted","volume":"382","author":"A Fauci","year":"2020","journal-title":"New England Journal of Medicine"},{"key":"pone.0345088.ref006","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1137\/S0036144500371907","article-title":"The mathematics of infectious diseases","volume":"42","author":"H Hethcote","year":"2000","journal-title":"SIAM Review"},{"issue":"12","key":"pone.0345088.ref007","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008409","article-title":"Practical considerations for measuring the effective reproductive number, Rt","volume":"16","author":"KM Gostic","year":"2020","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"pone.0345088.ref008","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1186\/s12889-017-4372-y","article-title":"Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats","volume":"17","author":"E Buckingham-Jeffery","year":"2017","journal-title":"BMC Public Health"},{"issue":"1","key":"pone.0345088.ref009","doi-asserted-by":"crossref","first-page":"102","DOI":"10.21037\/jtd.2017.12.115","article-title":"The enigma of the weekend effect","volume":"10","author":"A Mathew","year":"2018","journal-title":"J Thorac Dis"},{"key":"pone.0345088.ref010","first-page":"380","article-title":"Handbook of Biosurveillance","volume":"40","author":"S Khan","year":"2007","journal-title":"Journal of Biomedical Informatics"},{"issue":"12","key":"pone.0345088.ref011","article-title":"Singapore\u2019s COVID\u201019 Genomic Surveillance Programme: Strategies and Insights From a Pandemic Year","volume":"18","author":"HY Tan","year":"2024","journal-title":"Influenza and Other Respiratory Viruses"},{"key":"pone.0345088.ref012","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ijid.2022.05.041","article-title":"Whole-genome sequencing of SARS-CoV-2 reveals diverse mutations in circulating Alpha and Delta variants during the first, second, and third waves of COVID-19 in South Kivu, east of the Democratic Republic of the Congo","volume":"122","author":"PB Ntagereka","year":"2022","journal-title":"Int J Infect Dis"},{"issue":"9","key":"pone.0345088.ref013","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1093\/aje\/kwt133","article-title":"A new framework and software to estimate time-varying reproduction numbers during epidemics","volume":"178","author":"A Cori","year":"2013","journal-title":"Am J Epidemiol"},{"key":"pone.0345088.ref014","unstructured":"Alessandro A, Tommi A. 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