Developing a Spatial Domain Model for Optimizing Infectious Disease Control through Vaccine Distribution

Developing a Spatial Domain Model for Optimizing Infectious Disease Control through Vaccine Distribution | 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 Developing a Spatial Domain Model for Optimizing Infectious Disease Control through Vaccine Distribution Chun Li, Chao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4362000/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 The global emergence of infectious diseases, such as the coronavirus, has presented a formidable and widespread challenge, causing significant human casualties, property damage, and economic disruptions. Investigating infectious disease models becomes imperative for a comprehensive understanding of the dissemination patterns of highly contagious illnesses. Hence, research into infectious disease models is of utmost importance. This paper introduces the application of physics-constrained machine learning (PCML) to develop a spatial domain model for infectious disease control, specifically focusing on vaccine distribution. Traditionally, dynamical systems are employed in epidemiological models to forecast the temporal evolution and growth trajectories of highly infectious diseases. In this study, we reframe the SIR models, incorporating corresponding policies through dynamical systems. Utilizing the PCML algorithm, we derive approximate numerical solutions for the systems of dynamical partial differential equations (PDEs) with an acceptable margin of approximation error. Additionally, we present various numerical solutions to the PDEs under diverse scenarios. Epidemics control deep manifold Learning high-order geometric flow PDEs variational problems Full Text Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4362000","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299026376,"identity":"c844e2dc-4056-4f4f-92f3-d520a395d789","order_by":0,"name":"Chun Li","email":"","orcid":"","institution":"Shenzhen MSU-BIT University","correspondingAuthor":false,"prefix":"","firstName":"Chun","middleName":"","lastName":"Li","suffix":""},{"id":299026379,"identity":"42b691f2-5f4a-44f6-9d8a-58a790d4268a","order_by":1,"name":"Chao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYLACxgYGBn4GBjYYm0gtkg0kazE4QKwWg+NnD7/4ucNOzvj86bTHPAw2shsOMD97gFfLmbw0y94zycZmN3K3G/MwpBlvOMBmboBPi9mBHDNjxjbmxG03eLdJ8zAcTtxwgIdNAq+W829AWuoTN/efBWn5T4SWGznGjxnbgIYz5IK0HCCsxf7GGzPG3rbjxhJAvxjOMUg2nnmYzQyvFsn+HOMPP9uq5fiBDnvwpsJOtu948zO8WoAA2RmgoGImoB6k5ANhNaNgFIyCUTCiAQDqZUsKyalbxAAAAABJRU5ErkJggg==","orcid":"","institution":"Shenzhen MSU-BIT University","correspondingAuthor":true,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-03 05:12:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4362000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4362000/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62579828,"identity":"cbdf7a54-86db-4d8e-beae-9e4a49a07247","added_by":"auto","created_at":"2024-08-16 05:59:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1343347,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerArticle53.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4362000/v1_covered_b9461a59-9860-497f-93f0-2898c49a0da7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing a Spatial Domain Model for Optimizing Infectious Disease Control through Vaccine Distribution","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Epidemics control, deep manifold Learning, high-order geometric flow, PDEs, variational problems","lastPublishedDoi":"10.21203/rs.3.rs-4362000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4362000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global emergence of infectious diseases, such as the coronavirus, has presented a formidable and widespread challenge, causing significant human casualties, property damage, and economic disruptions. 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