Participatory Surveillance for One-Week-Ahead Local ILI Early Warning: A Prospective Evaluation of Statistical and Machine-Learning Approaches

Participatory Surveillance for One-Week-Ahead Local ILI Early Warning: A Prospective Evaluation of Statistical and Machine-Learning Approaches | 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 Article Participatory Surveillance for One-Week-Ahead Local ILI Early Warning: A Prospective Evaluation of Statistical and Machine-Learning Approaches Seunghoon (Kelly) Lee, Iman Hakim, Autumn Gertz, John Brownstein, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9314449/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Timely local early warning of rising influenza-like illness (ILI) activity can support short-term public health planning, including situational review, staffing, and resource allocation. We evaluated whether participatory symptom reports can provide reliable one-week-ahead warning of unusually high local ILI activity across U.S. metropolitan areas. Using Outbreaks Near Me reports from April 2020 through December 2024, we constructed a weekly Core Based Statistical Area panel and defined elevated activity within each target-year fold using a training-only upper-tail threshold on a stabilized symptom rate. We then conducted a strictly prospective year-ahead evaluation for target years 2022 through 2024, comparing regularized logistic models, gradient-boosted trees, deep sequence models, and time-ordered ensembles under a shared feature set and fold-safe preprocessing pipeline. Ensemble predictors achieved the strongest rare-event discrimination, with AUPRC 0.6473, outperforming the best single model, XGBoost, with AUPRC 0.6296. Among base models, XGBoost showed the strongest probability reliability, and post-hoc calibration improved the stacked ensemble without changing its rank performance. Overall, participatory surveillance retained meaningful one-week-ahead predictive signal for local surge-risk ranking under prospective evaluation. These findings suggest that participatory surveillance can support local early warning of unusually high ILI-related symptom activity, and that boosted and ensemble methods offer the strongest practical performance for short-horizon alerting in this setting. Health sciences/Diseases/Infectious diseases/Influenza virus Humanities/Health humanities Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted 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-9314449","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622970624,"identity":"1a48f835-e8dd-450d-aed9-31d830e97847","order_by":0,"name":"Seunghoon (Kelly) 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We evaluated whether participatory symptom reports can provide reliable one-week-ahead warning of unusually high local ILI activity across U.S. metropolitan areas. Using Outbreaks Near Me reports from April 2020 through December 2024, we constructed a weekly Core Based Statistical Area panel and defined elevated activity within each target-year fold using a training-only upper-tail threshold on a stabilized symptom rate. We then conducted a strictly prospective year-ahead evaluation for target years 2022 through 2024, comparing regularized logistic models, gradient-boosted trees, deep sequence models, and time-ordered ensembles under a shared feature set and fold-safe preprocessing pipeline. Ensemble predictors achieved the strongest rare-event discrimination, with AUPRC 0.6473, outperforming the best single model, XGBoost, with AUPRC 0.6296. 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