Uniform Information Coverage Drives Mean-Field Convergence and Reverse Influence in Multiplex Epidemic–Belief Systems

Uniform Information Coverage Drives Mean-Field Convergence and Reverse Influence in Multiplex Epidemic–Belief Systems | 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 Uniform Information Coverage Drives Mean-Field Convergence and Reverse Influence in Multiplex Epidemic–Belief Systems Anurodh Budhathoki, Habish Dhakal, Bibek Pudasaini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9240248/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 Epidemic trajectories are jointly determined by biological transmission dynamics and the social processes governing belief formation and behavioral adherence. This paper introduces a coupled epidemic–belief–compliance model embedded on a multiplex network to investigate how misinformation and fact-checking interact to shape infection outcomes. The framework combines an SEIR disease process operating on a physical contact layer with a bounded-confidence belief diffusion model on an information layer, capturing the opposing effects of misinformation propagation and corrective messaging. Through analytical derivation and large-scale Monte Carlo simulations (N = 10³–10⁴, 3,460 realizations), we show that uniformly distributed fact-checking meaningfully reduces epidemic peaks (by 26–39%), maintains higher compliance levels, and stabilizes belief dynamics compared to influencer-targeted interventions. Uniform coverage suppresses local network correlations, allowing the coupled system to converge toward a tractable mean-field representation; peak infection variance scales as Var ∝ N⁻¹, confirming self-averaging behavior as population size increases. A Graph Neural Network surrogate offers no predictive benefit over a Random Forest baseline (R² ≈ 0.98), further supporting the conclusion that fine-grained topological structure becomes uninformative under broad information coverage. We identify and formally characterize a reverse influence mechanism: well-informed peripheral nodes function as belief anchors that stabilize the attitudes of highly connected influencers, counteracting misinformation accumulation at network hubs. This feedback loop explains why uniform coverage outperforms hub-targeted strategies even in polarized or clustered networks, where targeted approaches are conventionally assumed to hold an advantage. These results provide a theoretical basis for designing public health communication strategies that prioritize inclusiveness and consistency over precision targeting, with direct implications for misinformation-resilient epidemic response at population scale. Epidemiology Sociology multiplex networks epidemic-belief coupling misinformation dynamics fact- checking interventions mean-field approximation behavioral epidemiology information diffusion self-averaging 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-9240248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613048567,"identity":"42e5e4e1-d828-4ccc-9f7f-9f8dd513434d","order_by":0,"name":"Anurodh Budhathoki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYNACtgMMDMzMQIJBQoYELexsCSAtPCRo4ecxADEJa+Gf3WP24UPZnWh+Zp7Pr27UWPAwsB8+ugGfFok7Z4xnzjj3LHdmM+8265xjQIfxpKXdwGvNjRxjZt62w7kbDvNuM85hA2qR4DHDq0UepmX/YZ5nxjn/iNBiALeFmYf5cW4bEVoMb6QVM844dzh3xmE2M+bcPgkeNkJ+kbuRvJnhQ9nh3P7+w48/53yrk+NnP3wMv/eRAJsEmCRWOQgwfyBF9SgYBaNgFIwcAABes0e+5ifavwAAAABJRU5ErkJggg==","orcid":"","institution":"Unaffiliated","correspondingAuthor":true,"prefix":"","firstName":"Anurodh","middleName":"","lastName":"Budhathoki","suffix":""},{"id":613048568,"identity":"c6bd5c5b-af1a-4a5f-9c4c-61e6b31beae8","order_by":1,"name":"Habish Dhakal","email":"","orcid":"","institution":"Unaffiliated","correspondingAuthor":false,"prefix":"","firstName":"Habish","middleName":"","lastName":"Dhakal","suffix":""},{"id":613048569,"identity":"f9d80ec5-261f-464a-80d8-b49a59fbb6e3","order_by":2,"name":"Bibek Pudasaini","email":"","orcid":"","institution":"Unaffiliated","correspondingAuthor":false,"prefix":"","firstName":"Bibek","middleName":"","lastName":"Pudasaini","suffix":""}],"badges":[],"createdAt":"2026-03-27 05:17:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9240248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9240248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105728877,"identity":"b181b4f7-a999-4111-826f-54f842a7ef6b","added_by":"auto","created_at":"2026-03-30 11:12:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1179787,"visible":true,"origin":"","legend":"","description":"","filename":"UniformInformationCoverageDrivesMeanFieldConvergenceandReverseInfluenceinMultiplexEpidemicBeliefSystems.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9240248/v1_covered_695be2a1-bc99-40db-9dd7-879f30846c79.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eUniform Information Coverage Drives Mean-Field Convergence and Reverse Influence in Multiplex Epidemic–Belief Systems\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SecurityPal AI","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":"multiplex networks, epidemic-belief coupling, misinformation dynamics, fact- checking interventions, mean-field approximation, behavioral epidemiology, information diffusion, self-averaging","lastPublishedDoi":"10.21203/rs.3.rs-9240248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9240248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpidemic trajectories are jointly determined by biological transmission dynamics and the social processes governing belief formation and behavioral adherence. This paper introduces a coupled epidemic–belief–compliance model embedded on a multiplex network to investigate how misinformation and fact-checking interact to shape infection outcomes. The framework combines an SEIR disease process operating on a physical contact layer with a bounded-confidence belief diffusion model on an information layer, capturing the opposing effects of misinformation propagation and corrective messaging.\u003c/p\u003e\n\u003cp\u003eThrough analytical derivation and large-scale Monte Carlo simulations (N = 10³–10⁴, 3,460 realizations), we show that uniformly distributed fact-checking meaningfully reduces epidemic peaks (by 26–39%), maintains higher compliance levels, and stabilizes belief dynamics compared to influencer-targeted interventions. Uniform coverage suppresses local network correlations, allowing the coupled system to converge toward a tractable mean-field representation; peak infection variance scales as Var ∝ N⁻¹, confirming self-averaging behavior as population size increases. A Graph Neural Network surrogate offers no predictive benefit over a Random Forest baseline (R² ≈ 0.98), further supporting the conclusion that fine-grained topological structure becomes uninformative under broad information coverage.\u003c/p\u003e\n\u003cp\u003eWe identify and formally characterize a reverse influence mechanism: well-informed peripheral nodes function as belief anchors that stabilize the attitudes of highly connected influencers, counteracting misinformation accumulation at network hubs. This feedback loop explains why uniform coverage outperforms hub-targeted strategies even in polarized or clustered networks, where targeted approaches are conventionally assumed to hold an advantage. These results provide a theoretical basis for designing public health communication strategies that prioritize inclusiveness and consistency over precision targeting, with direct implications for misinformation-resilient epidemic response at population scale.\u003c/p\u003e","manuscriptTitle":"Uniform Information Coverage Drives Mean-Field Convergence and Reverse Influence in Multiplex Epidemic–Belief Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 05:27:13","doi":"10.21203/rs.3.rs-9240248/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"f6c544e6-be38-4c99-a38a-4e735c7815a7","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65238115,"name":"Epidemiology"},{"id":65238116,"name":"Sociology"}],"tags":[],"updatedAt":"2026-03-30T05:27:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 05:27:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9240248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9240248","identity":"rs-9240248","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}