Identifying Critical Viral Load Thresholds in HBV-HDV Superinfection: A Mathematical Modelling Approach

Identifying Critical Viral Load Thresholds in HBV-HDV Superinfection: A Mathematical Modelling Approach | 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 Identifying Critical Viral Load Thresholds in HBV-HDV Superinfection: A Mathematical Modelling Approach Trishithsatya Repalle This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6645738/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 Approximately 254 million people live with hepatitis B virus (HBV) globally, with 12 million having hepatitis D virus (HDV) coinfection [1]. While research suggests HBV viral load influences HDV superinfection risk [2], the precise relationship remains poorly understood. This paper proposes a mathematical model to identify critical viral load thresholds determining HDV superinfection probability. We develop a six-dimensional system of ordinary differential equations incorporating susceptible, HBV-infected, HDV-coinfected, under-treatment, recovered, and viral load compartments [5]. A novel feature is the inclusion of a continuous viral load-dependent probability function for HDV superinfection, implemented through a sigmoidal threshold. Using next-generation matrix methodology [3], we derive basic reproduction numbers and identify three equilibrium states. Stability and bifurcation analyses reveal that HDV superinfection probability remains low below an HBV viral load threshold of ~10^5 IU/mL but exceeds 80% above 10^8 IU/mL. Model simulations calibrated with WHO 2024 data [1] suggest early HBV viral load suppression (within 6 months of infection) can reduce HDV superinfection risk by up to 60%, whereas delayed treatment (beyond 12 months) results in chronic coinfection in over 70% of cases. Sensitivity analysis indicates that lowering HBV viral load by one log unit can decrease HDV transmission rates by 30-40%. This mathematical framework provides testable hypotheses for HDV superinfection thresholds and offers insights for optimizing treatment timing [2]. These findings have direct implications for clinical strategies in managing HBV-HDV coinfection. Systems Biology Virology HBV-HDV superinfection viral load threshold mathematical modeling Full Text Additional Declarations The authors declare no competing interests. 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-6645738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455657854,"identity":"630d4538-a426-436e-91cf-938599d439ed","order_by":0,"name":"Trishithsatya Repalle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYFACxgZmBgMo+0EBgxyIPvAAjwYeiBaongQDBmOwlgS8WhgYmBkYEFoSG8AMPFrspQ83fy4o+MNg3t578EOCgU36/LDDD4G22MnpNuCwhS+xTXoG0GEyZ84lSyQYpOVuvJ1mANSSbGx2AIcWHsY2Zh6gFgmJHAOglsO5G2cngLQcSNyGW0vzZ7AW+TfGPxIM/qcbzk7/QEhLgzTEFh4zCZD58tI5BGw5w9gG1GLMI8GTl2aRYJBsuEE6p+AAkIHTL+w97I8/8/yRk5NgP3v4xocKO3n52embPwAZcri0wG2DRBEQGIBVGuBRi6ILBOQbiFI9CkbBKBgFIwgAAP7EVe6a/+2iAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Trishithsatya","middleName":"","lastName":"Repalle","suffix":""}],"badges":[],"createdAt":"2025-05-12 10:38:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6645738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6645738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82672039,"identity":"787cb3e7-a81f-47cb-ad07-903c9077309a","added_by":"auto","created_at":"2025-05-14 03:08:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":342803,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptSubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6645738/v1_covered_e4bc4477-ad7d-44e8-87cd-cbdbf385494e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIdentifying Critical Viral Load Thresholds in HBV-HDV Superinfection: A Mathematical Modelling Approach\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Rouse High School","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":"HBV-HDV superinfection, viral load threshold, mathematical modeling","lastPublishedDoi":"10.21203/rs.3.rs-6645738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6645738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eApproximately 254 million people live with hepatitis B virus (HBV) globally, with 12 million having hepatitis D virus (HDV) coinfection [1]. 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