A Comparison of Change Point Detection Methods for Pest Outbreak Detection

A Comparison of Change Point Detection Methods for Pest Outbreak Detection | 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 Comparison of Change Point Detection Methods for Pest Outbreak Detection Huidi Ma, Benjamin D. Leibowicz, John J. Hasenbein This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7908521/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Environment Systems and Decisions → Version 1 posted 10 You are reading this latest preprint version Abstract This study evaluates the application of change point detection (CPD) methodologies in the Agricultural Quarantine Inspection Monitoring (AQIM) program to identify significant shifts in pest arrival rates. Unlike prior work that focused on optimizing sampling strategies, this paper emphasizes the numerical evaluation of CPD algorithms for detecting pest outbreaks using AQIM data. The objective is to assess whether these methods can reliably detect and signal outbreaks when pest arrival rates exceed critical thresholds. Through extensive computational experiments, we compare the accuracy, sensitivity, and robustness of CPD methods under various outbreak scenarios and data conditions. The results demonstrate that CPD techniques, particularly cumulative sum (CUSUM) and exponential moving average (EMA), achieve high detection rates and low false alarm rates, even in challenging settings such as small outbreaks. These findings highlight the feasibility of integrating CPD algorithms into AQIM operations, providing a scalable and practical approach to enhancing outbreak detection and strengthening agricultural biosecurity in the context of global trade and evolving pest risks. change point detection pest outbreak detection biosecurity sampling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Environment Systems and Decisions → Version 1 posted Editorial decision: Revision requested 23 Dec, 2025 Reviews received at journal 18 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers invited by journal 02 Nov, 2025 Editor assigned by journal 01 Nov, 2025 Submission checks completed at journal 23 Oct, 2025 First submitted to journal 20 Oct, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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