A Two-Pronged Analytical Approach to Transmission Dynamics with Spatial Heterogeneity: Epidemic Curve Profiling and Mobility-Adjusted Rt Estimation

A Two-Pronged Analytical Approach to Transmission Dynamics with Spatial Heterogeneity: Epidemic Curve Profiling and Mobility-Adjusted Rt 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 A Two-Pronged Analytical Approach to Transmission Dynamics with Spatial Heterogeneity: Epidemic Curve Profiling and Mobility-Adjusted Rt Estimation Young Kim, Junwoo Jo, Byul Nim Kim, Arsen Abdulali, Gerardo Chowell, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6954754/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 12 You are reading this latest preprint version Abstract Background. Capturing spatial heterogeneity in disease transmission is essential for effective and timely public health interventions. However, conventional methods for estimating the effective reproduction number (Rt) often assume homogeneous mixing and may not adequately capture spatial connectivity—such as human mobility—potentially obscuring local transmission risks. Shape-based analyses of epidemic curves characterize timing and recurrence of waves but remain descriptive and cannot quantify transmission intensity. Because each approach offers only a partial view of regional dynamics, an integrative framework is needed to capture both epidemic structure and dynamics. Methods. We present an integrative approach that couples mobility-adjusted Rt estimation with shape-based clustering of epidemic growth curves. Using COVID-19 case and mobility data from 17 South-Korean provinces during the first wave (February–April 2020), we estimate Rt within a sliding window that incorporates inter-regional mobility matrices derived from telecommunication records, thereby weighting cross-regional transmission potential. Simultaneously, we apply dynamic time warping and hierarchical clustering to align and group epidemic curves based on morphological similarity. Results. Our analysis reveals three distinct regional outbreak profiles: (1) prolonged, staggered outbreaks in high-mobility metropolitan areas; (2) synchronized, sharp surges followed by rapid suppression in southeastern provinces centered on the Daegu superspreading event; and (3) brief, self-contained outbreaks in isolated regions such as Sejong. Regions with comparable Rt trajectories sometimes displayed markedly different curve shapes, indicating that neither Rt nor curve morphology alone fully captures spatial transmission dynamics. In metropolitan areas, the mobility-adjusted Rt rose earlier than visible case surges, suggesting its utility as an early indicator of latent transmission pressure. Conclusions. By coupling structural shape analysis with mobility-aware Rt estimation, our integrative framework provides a richer, more actionable understanding of subnational epidemic dynamics. This dual perspective enables earlier detection of transmission risks and supports geographically targeted interventions. Our methodology is broadly applicable to emerging diseases where both spatial connectivity and temporal dynamics drive transmission. Effective Reproduction Number Spatial Heterogeneity Mobility Shape Analysis Clustering Epidemic Curve COVID-19 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 06 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviews received at journal 02 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 23 Jun, 2025 Submission checks completed at journal 23 Jun, 2025 First submitted to journal 23 Jun, 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-6954754","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476896812,"identity":"41481f1d-5c25-4fc7-ab10-5e1078c92719","order_by":0,"name":"Young Kim","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Young","middleName":"","lastName":"Kim","suffix":""},{"id":476896813,"identity":"5447bd60-d5ca-4d37-9b77-eecec7725882","order_by":1,"name":"Junwoo Jo","email":"","orcid":"","institution":"Kyung Hee 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However, conventional methods for estimating the effective reproduction number (Rt) often assume homogeneous mixing and may not adequately capture spatial connectivity—such as human mobility—potentially obscuring local transmission risks. Shape-based analyses of epidemic curves characterize timing and recurrence of waves but remain descriptive and cannot quantify transmission intensity. Because each approach offers only a partial view of regional dynamics, an integrative framework is needed to capture both epidemic structure and dynamics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e We present an integrative approach that couples mobility-adjusted Rt estimation with shape-based clustering of epidemic growth curves. Using COVID-19 case and mobility data from 17 South-Korean provinces during the first wave (February–April 2020), we estimate Rt within a sliding window that incorporates inter-regional mobility matrices derived from telecommunication records, thereby weighting cross-regional transmission potential. Simultaneously, we apply dynamic time warping and hierarchical clustering to align and group epidemic curves based on morphological similarity. \u003cstrong\u003eResults.\u003c/strong\u003e Our analysis reveals three distinct regional outbreak profiles: (1) prolonged, staggered outbreaks in high-mobility metropolitan areas; (2) synchronized, sharp surges followed by rapid suppression in southeastern provinces centered on the Daegu superspreading event; and (3) brief, self-contained outbreaks in isolated regions such as Sejong. Regions with comparable Rt trajectories sometimes displayed markedly different curve shapes, indicating that neither Rt nor curve morphology alone fully captures spatial transmission dynamics. In metropolitan areas, the mobility-adjusted Rt rose earlier than visible case surges, suggesting its utility as an early indicator of latent transmission pressure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e By coupling structural shape analysis with mobility-aware Rt estimation, our integrative framework provides a richer, more actionable understanding of subnational epidemic dynamics. This dual perspective enables earlier detection of transmission risks and supports geographically targeted interventions. 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