Extending Peaks-Over-Threshold Methods to Public Health: Modified Threshold Selection and Scenario-Based Risk Estimation

Extending Peaks-Over-Threshold Methods to Public Health: Modified Threshold Selection and Scenario-Based Risk Estimation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Extending Peaks-Over-Threshold Methods to Public Health: Modified Threshold Selection and Scenario-Based Risk Estimation Farabe Khan Alif Alif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7553933/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Health Services and Outcomes Research Methodology → Version 1 posted 10 You are reading this latest preprint version Abstract Rare but extreme events in public health, such as sudden dengue outbreaks or surges in urgent cancer referrals, pose disproportionate threats to healthcare systems by overwhelming diagnostic capacity, straining resources, and elevating mortality risks. Traditional statistical approaches, which emphasize average behavior, often fail to capture these high-impact tail risks. This study advances the application of Extreme Value Theory (EVT) in public health by extending the peaks-over-threshold (POT) framework to short, structured medical time series. We introduce a modified AD-L threshold selection procedure, tailored for small-sample health datasets, which enhances robustness and objectivity compared to conventional graphical diagnostics. Using monthly dengue prevalence data from Bangladesh (2008–2023) and urgent cancer referrals from Wales (2005–2020), we demonstrate a systematic workflow that integrates stationarity testing, STL decomposition, transformation through differencing, and scenario-based tail modeling. Our results show that the modified AD-L method yields stable thresholds and consistent generalized Pareto distribution fits across both datasets, including when analyses are restricted to positive increments—capturing the public health priority of sudden increases rather than declines. Return level estimates quantify the scale of extreme month-to-month surges, offering forward-looking insights into outbreak severity and system demand. Methodologically, this study bridges a gap by adapting POT methods for medical data, while practically, it equips decision-makers with interpretable metrics of extreme health shocks. Limitations include reliance on differencing for non-stationarity and the scope of monthly data, but the framework remains generalizable to other health outcomes. By emphasizing both statistical rigor and actionable interpretation, this research positions EVT as a vital tool for anticipating and managing rare but consequential public health extremes. Extreme value Threshold Goodness of fit Public health Generalized Pareto distribution Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Health Services and Outcomes Research Methodology → Version 1 posted Editorial decision: Revision requested 06 Dec, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers invited by journal 20 Sep, 2025 Editor assigned by journal 20 Sep, 2025 Submission checks completed at journal 07 Sep, 2025 First submitted to journal 07 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7553933","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519230263,"identity":"c84684c5-abe8-463a-8b18-3d061f39d132","order_by":0,"name":"Farabe Khan Alif Alif","email":"data:image/png;base64,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","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Farabe","middleName":"Khan Alif","lastName":"Alif","suffix":""}],"badges":[],"createdAt":"2025-09-07 04:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7553933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7553933/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10742-026-00378-9","type":"published","date":"2026-03-05T15:59:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92553076,"identity":"0472ba62-73eb-46a5-b06b-d1e99104398d","added_by":"auto","created_at":"2025-10-01 01:59:50","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4411,"visible":true,"origin":"","legend":"","description":"","filename":"dcc251933a084daeafa6b4c068a68f89.json","url":"https://assets-eu.researchsquare.com/files/rs-7553933/v1/a2ac197898bc097f2ad6a8ac.json"},{"id":104252010,"identity":"068b6fee-4d25-4d97-b902-511eee20bf19","added_by":"auto","created_at":"2026-03-09 16:16:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":407814,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7553933/v1_covered_7b173b1a-76dc-4ce4-8df2-56e371095ff7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Extending Peaks-Over-Threshold Methods to Public Health: Modified Threshold Selection and Scenario-Based Risk Estimation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"health-services-and-outcomes-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hsor","sideBox":"Learn more about [Health Services and Outcomes Research Methodology](http://link.springer.com/journal/10742)","snPcode":"10742","submissionUrl":"https://submission.nature.com/new-submission/10742/3","title":"Health Services and Outcomes Research Methodology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Extreme value, Threshold, Goodness of fit, Public health, Generalized Pareto distribution","lastPublishedDoi":"10.21203/rs.3.rs-7553933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7553933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Rare but extreme events in public health, such as sudden dengue outbreaks or surges in urgent cancer referrals, pose disproportionate threats to healthcare systems by overwhelming diagnostic capacity, straining resources, and elevating mortality risks. 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