Swarm-optimized numerical investigation of Dengue Fever Model

Swarm-optimized numerical investigation of Dengue Fever Model | 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 Swarm-optimized numerical investigation of Dengue Fever Model Farhad Muhammad Riaz, Raja Muhammad Shamayel Ullah, Areej Alasiry, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4346166/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Soft Computing → Version 1 posted 5 You are reading this latest preprint version Abstract The current study aims to present a swarm-optimized technique for the numerical treatment of dengue fever non-linear model. The model is composed of the coupled nonlinear system comprising the susceptible, infected, and recovered compartments. The system is transformed into an unsupervised single layer feed-forward artificial neural network with a Mexican hat wavelet activation function in the hidden layer. The unknowns of the neural network is optimized with particle swarm optimization as an efficient global search aided by the effective local search technique based on sequential quadratic programming. The presented results are compared with state of art Runge-Kutta method and other modern reported techniques on various performance indicators like absolute error, mean average deviation, global absolute error, global mean average deviation, convergence, and computational complexity. Comprehensive Monte Carlo simulations and their statistical analysis are presented to ensure accuracy, consistency in convergence, and computational complexity in terms of execution time. It is observed that the proposed scheme is accurate, reliable, convergent, and computationally viable in treating the nonlinear coupled system under consideration. Coupled system of differential equations Hybrid optimization dengue fever model Monte Carlo simulations. Full Text Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 14 May, 2024 Reviewers invited by journal 13 May, 2024 Editor invited by journal 07 May, 2024 Editor assigned by journal 04 May, 2024 First submitted to journal 01 May, 2024 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-4346166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":302109701,"identity":"246cfc48-eb02-4a5b-be4a-62cdcb21ed7c","order_by":0,"name":"Farhad Muhammad Riaz","email":"","orcid":"","institution":"HITEC University","correspondingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"Muhammad","lastName":"Riaz","suffix":""},{"id":302109702,"identity":"37788df5-d9d6-4f0f-a94e-bf1aedd11fdd","order_by":1,"name":"Raja Muhammad Shamayel Ullah","email":"","orcid":"","institution":"HITEC University","correspondingAuthor":false,"prefix":"","firstName":"Raja","middleName":"Muhammad Shamayel","lastName":"Ullah","suffix":""},{"id":302109703,"identity":"48154a8d-b426-492b-8b9b-575f5ba1b8f6","order_by":2,"name":"Areej Alasiry","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Areej","middleName":"","lastName":"Alasiry","suffix":""},{"id":302109704,"identity":"201a325b-f098-404d-9433-b70065809d53","order_by":3,"name":"Mehrez Marzougui","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Mehrez","middleName":"","lastName":"Marzougui","suffix":""},{"id":302109705,"identity":"3394285b-2639-4869-8d25-60e5930894fd","order_by":4,"name":"Junaid Ali Khan","email":"data:image/png;base64,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","orcid":"","institution":"HITEC University","correspondingAuthor":true,"prefix":"","firstName":"Junaid","middleName":"Ali","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2024-04-30 04:21:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4346166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4346166/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00500-025-10868-6","type":"published","date":"2025-09-29T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92884610,"identity":"88605e01-397a-440f-b6c8-367330b61b81","added_by":"auto","created_at":"2025-10-06 16:13:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1185071,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4346166/v1_covered_71e81a68-491d-4729-a54e-daebf9951c68.pdf"}],"financialInterests":"","formattedTitle":"Swarm-optimized numerical investigation of Dengue Fever Model","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coupled system of differential equations, Hybrid optimization, dengue fever model, Monte Carlo simulations. 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