Modelling Meningitis Transmission with Booster Vaccination and Resistance Dynamics: Equilibrium and Control Analyses

Modelling Meningitis Transmission with Booster Vaccination and Resistance Dynamics: Equilibrium and Control Analyses | 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 Modelling Meningitis Transmission with Booster Vaccination and Resistance Dynamics: Equilibrium and Control Analyses Richmond Balinia Adda, Gideon Mensah Engmann, Elijah B. Baloba, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7585367/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 Meningitis remains a global health threat due to its high fatality, epidemic potential, and rising antibiotic resistance. While mathematical models exist, previous studies often neglected the combined dynamics of booster vaccination waning and antibiotic resistance evolution, limiting their ability to inform optimal control strategies. To bridge this gap, we develop a novel deterministic compartmental model incorporating six epidemiological compartments, distinct primary/booster vaccination pathways with waning immunity, and resistance emergence via mutation. Using equilibrium analysis and stability theory (Routh-Hurwitz, Lyapunov functions), we establish critical thresholds: the basic reproduction number ($R_0$) determines disease extinction ($R_0 1$), and both meningitis-free and endemic equilibria are globally stable when $R_0 1$, respectively. Sensitivity analysis via next-generation matrices reveals that $R_0$ is most significantly influenced by contact rates ($\alpha_m$, $\alpha_r$), vaccination rates ($\kappa$, $\chi$), recovery rates ($\tau_m$, $\tau_r$), and the resistance mutation rate ($\xi$). Crucially, our results demonstrate that increasing booster coverage ($\chi > 0.15$) and reducing resistance mutation ($\xi < 0.01$) are paramount for driving $R_0 0.25$, resistance containment) provides synergistic, non-linear reductions in transmission. This model explicitly integrates booster dynamics and resistance and emergence, provides a realistic framework for optimising vaccination programs and antibiotic stewardship, particularly in settings like the African Meningitis Belt, aligning with WHO roadmap targets. Computational Biology Mathematical and Theoretical Biology Biostatistics Epidemiology Computational Mathematics Applied Mathematics Meningitis transmission Mathematical modelling Booster vaccination Antibiotic resistance Basic reproduction number Stability analysis 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-7585367","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513255033,"identity":"281a27b1-d192-48de-8a7b-f96a80863ce2","order_by":0,"name":"Richmond Balinia Adda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACZgY2EMXYwJBgwPAByGJjJ0UL4wyQFmbC9iC0MPNADMEPzNuZnz26UXFHtr89eeNnm1/b5PmYGRg/fMzBrUXmMJu5cc6ZZ8Yzzjwrls7tu23YxszALDlzG24tEsw8bNK5bYcTG27kGEjn9txmBGphY+YlRsv8GznGvy17btsTr2XDjRwzaYYftxOJ0MJmJp1z5rDxxjPPyix7G24ntzEzNuP3C//hZ9I5FYdl5x1P3nzjx5/btvPbmw9++IhHCypgbAOTDcSqB4E/pCgeBaNgFIyCkQIAioVPt8eBAK8AAAAASUVORK5CYII=","orcid":"","institution":"Department of Biometry, School of Mathematical Sciences, C. 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While mathematical models exist, previous studies often neglected the combined dynamics of booster vaccination waning and antibiotic resistance evolution, limiting their ability to inform optimal control strategies. To bridge this gap, we develop a novel deterministic compartmental model incorporating six epidemiological compartments, distinct primary/booster vaccination pathways with waning immunity, and resistance emergence via mutation. Using equilibrium analysis and stability theory (Routh-Hurwitz, Lyapunov functions), we establish critical thresholds: the basic reproduction number ($R_0$) determines disease extinction ($R_0 \u0026lt; 1$) or endemicity ($R_0 \u0026gt; 1$), and both meningitis-free and endemic equilibria are globally stable when $R_0 \u0026lt; 1$ and $R_0 \u0026gt; 1$, respectively. Sensitivity analysis via next-generation matrices reveals that $R_0$ is most significantly influenced by contact rates ($\\alpha_m$, $\\alpha_r$), vaccination rates ($\\kappa$, $\\chi$), recovery rates ($\\tau_m$, $\\tau_r$), and the resistance mutation rate ($\\xi$). Crucially, our results demonstrate that increasing booster coverage ($\\chi \u0026gt; 0.15$) and reducing resistance mutation ($\\xi \u0026lt; 0.01$) are paramount for driving $R_0 \u0026lt; 1$. Combining interventions (booster durability, neonatal immunisation $\\kappa \u0026gt; 0.25$, resistance containment) provides synergistic, non-linear reductions in transmission. This model explicitly integrates booster dynamics and resistance and emergence, provides a realistic framework for optimising vaccination programs and antibiotic stewardship, particularly in settings like the African Meningitis Belt, aligning with WHO roadmap targets.\u003c/p\u003e","manuscriptTitle":"Modelling Meningitis Transmission with Booster Vaccination and Resistance Dynamics: Equilibrium and Control Analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 02:27:10","doi":"10.21203/rs.3.rs-7585367/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":"ba394ebd-b750-419f-aee5-f2fd5bbd0a7d","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54526945,"name":"Computational Biology"},{"id":54526946,"name":"Mathematical and Theoretical Biology"},{"id":54526947,"name":"Biostatistics"},{"id":54526948,"name":"Epidemiology"},{"id":54526949,"name":"Computational Mathematics"},{"id":54526950,"name":"Applied Mathematics"}],"tags":[],"updatedAt":"2025-09-12T02:27:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 02:27:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7585367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7585367","identity":"rs-7585367","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}