Dynamical Analysis of a New Epidemiological Model for the Control of Malaria–Tuberculosis Co-infection in Nigeria

Dynamical Analysis of a New Epidemiological Model for the Control of Malaria–Tuberculosis Co-infection in Nigeria | 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 Article Dynamical Analysis of a New Epidemiological Model for the Control of Malaria–Tuberculosis Co-infection in Nigeria Omeje Daniel, Acheneje Godwin Onuche, Agbata Benedict Celestine, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7417913/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 main focus of this work is the formulation of a deterministic mathematical model governed by a system of non-linear differential equations to investigate the dynamics of co-infection between tuberculosis and malaria. A thorough analysis of the model shows that the co-infection model exhibits the phenomenon of backward bifurcation when the reproduction number of the diseases were less than unity. To determine the key parameters that greatly influenced the spread of the diseases, we conducted sensitivity analysis of the reproduction number. Real life data pertaining to the two diseases were gathered and fitted to our model through which key parameter values embedded in the model were estimated. Using these parameter values, we conducted numerical simulations of the co-infection model. The results from the simulation showed that an increase in the rate of contact and the rate at which the individuals in the exposed classes proceed to the infected classes will cause the prevalence of these diseases to rise in the human population. On the other hand, expectedly, it was discovered that a rise in the treatment rates for those singly infected with malaria and TB, as well as those with their co-infection, will ultimately decrease the burden of Malaria-TB co-infection. We proceeded to develop predictive tool through which forecast is made about the endemicity of the diseases under varying prevailing circumstances. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Physical sciences/Mathematics and computing Epidemiology Model Analysis Reproduction number Co-infection 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. 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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-7417913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":512610278,"identity":"66b6ffa5-f2ce-4673-8aee-f7f07805a5af","order_by":0,"name":"Omeje Daniel","email":"","orcid":"","institution":"Prince Abubakar Audu University","correspondingAuthor":false,"prefix":"","firstName":"Omeje","middleName":"","lastName":"Daniel","suffix":""},{"id":512610279,"identity":"28430c1b-003f-4252-865e-b7b8545f90c2","order_by":1,"name":"Acheneje Godwin Onuche","email":"","orcid":"","institution":"Prince Abubakar Audu 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A thorough analysis of the model shows that the co-infection model exhibits the phenomenon of backward bifurcation when the reproduction number of the diseases were less than unity. To determine the key parameters that greatly influenced the spread of the diseases, we conducted sensitivity analysis of the reproduction number. Real life data pertaining to the two diseases were gathered and fitted to our model through which key parameter values embedded in the model were estimated. Using these parameter values, we conducted numerical simulations of the co-infection model. The results from the simulation showed that an increase in the rate of contact and the rate at which the individuals in the exposed classes proceed to the infected classes will cause the prevalence of these diseases to rise in the human population. 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