Assessing the accuracy of kernel-fitting and projections of spatial risk at key early timepoints in outbreaks

Assessing the accuracy of kernel-fitting and projections of spatial risk at key early timepoints in outbreaks | 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 Assessing the accuracy of kernel-fitting and projections of spatial risk at key early timepoints in outbreaks Simin Lee, Christopher M. Baker, Emily Sellens, Mark A. Stevenson, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9307461/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Rapid decision-making during exotic animal disease outbreaks necessitates the early characterisation of transmission dynamics; however, in practice, a strategic balance between accuracy and speed is required. This study presents a simplified and practical approach for field response, leveraging initial kernel estimates from spatial data to predict outbreak risks without the need for high-dimensional data or complex models. Using the 2001 United Kingdom foot-and-mouth disease outbreak as a case study, we identified the minimum data requirements for estimating stable transmission kernels during the early stages of an outbreak and evaluated their applicability as short-term predictive tools for field operations. Focusing on the five most affected regions, we estimated the optimal kernels on a weekly basis during the first month and monthly thereafter, validating their stability against full-outbreak datasets. Furthermore, we analysed the predictive accuracy for disease spatial spread within a seven-day window by calculating infectious pressure from these weekly updated kernels. Our analysis revealed that the power law type 2 kernel was consistently selected as the best fitting kernel across all study regions. Furthermore, the results demonstrated that reliable kernel estimation became feasible within clusters as early as seven days post-notification, or once a threshold of at least ten cumulative infected premises was reached. The kernel-based risk estimation had high sensitivity and negative predictive value, reliably identifying ‘low-risk’ areas. These findings demonstrate that even with minimal initial data and without complex parameters, it is possible to generate practically valid biosecurity guidelines. The proposed real-time kernel approach serves as a strategic tool to support evidence-based, rapid decision-making during the early phases of an animal disease outbreak, enabling the prioritised allocation of limited resources and the flexible setting of intervention thresholds. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research spatial transmission risk transmission kernel early decision-support Full Text Additional Declarations No competing interests reported. Supplementary Files FMDkernelSupplements20032026.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 11 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 02 Apr, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9307461","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":626103454,"identity":"ca6885ac-fb24-4b89-b17b-6913cded2d85","order_by":0,"name":"Simin Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3RsUrEMBzH8V8p5AZzds2R4/IKKV0ET3yVFtcbOonbFQpxOl0rKL5CQehqS8DJ8wW6KIKTgnDgooO5dhIMBSeHfJc0IR/yhwIu13/MB2pgDxjlZpXALjOndJiYa/Qu7ggZJH3mGlvI7nOQiNOdRKdgswCLD56m1YzwzHt8UxCy/p1IPS51ARZNsnXFC9lGZFr74ZVCWNqIbwgFS8pmVXEq20SxmPCxgmcjIu/J8lbTly1ZGjL6NOTQRqB7EktCyZbExLziG5JYB+uIZGFxT6J9Q0I1bfLJ5QM7urANdr6+2dCTuQiun59a+tWKgOfN++vx/ODMNlj/1o+dl6H7Uy6Xy+X6c99AmFJP7SQQJwAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Simin","middleName":"","lastName":"Lee","suffix":""},{"id":626103458,"identity":"abf43e1d-579b-4278-b07d-8c4c6cfe6265","order_by":1,"name":"Christopher M. 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This study presents a simplified and practical approach for field response, leveraging initial kernel estimates from spatial data to predict outbreak risks without the need for high-dimensional data or complex models. Using the 2001 United Kingdom foot-and-mouth disease outbreak as a case study, we identified the minimum data requirements for estimating stable transmission kernels during the early stages of an outbreak and evaluated their applicability as short-term predictive tools for field operations. Focusing on the five most affected regions, we estimated the optimal kernels on a weekly basis during the first month and monthly thereafter, validating their stability against full-outbreak datasets. Furthermore, we analysed the predictive accuracy for disease spatial spread within a seven-day window by calculating infectious pressure from these weekly updated kernels. 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