A Comprehensive Analysis of Fractional-Order Model of Tuberculosis with Treatment Intervention

A Comprehensive Analysis of Fractional-Order Model of Tuberculosis with Treatment Intervention | 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 A Comprehensive Analysis of Fractional-Order Model of Tuberculosis with Treatment Intervention Agbata Benedict Celestine, Raimonda Dervishi, Agbebaku Dennis Ferdinard, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6048692/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Aug, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 19 You are reading this latest preprint version Abstract Tuberculosis (TB) remains one of the top infectious disease killers worldwide, with an estimated 10.6 million new cases and 1.3 million deaths reported in 2022 alone (WHO, 2023). The COVID-19 pandemic has further disrupted TB control efforts by limiting access to healthcare services, interrupting treatment regimens, and delaying diagnoses leading to a resurgence in TB transmission It is caused by Mycobacterium tuberculosis and spread through the air, TB poses a serious threat, particularly to vulnerable groups such as individuals with weakened immune systems, including those living with HIV. These challenges emphasize the need for more robust and realistic modeling approaches to inform policy and intervention. In this study, we developed a fractional-order mathematical model to better understand how TB spreads and how it can be controlled. Our model divides the population into six key groups: those susceptible to infection, exposed individuals, people with acute TB, those with chronic TB, individuals undergoing treatment, and those who have recovered. To capture the complexities of TB transmission, we incorporated fractional-order derivatives along with the Adams-Bashforth method, allowing us to account for memory effects and more accurately reflect real-world dynamics. Through sensitivity analysis, we found that increasing treatment rates significantly boosts recovery among infected individuals. Our simulations also explored various intervention strategies, such as improving access to treatment, reducing diagnostic delays, and addressing non-linear transmission patterns. The results highlight the effectiveness of these measures in curbing TB spread and offer insights for improving disease control efforts. Tuberculosis (TB) Fractional-order derivatives Adams-Bashforth method Memory effects Sensitivity analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Aug, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 05 May, 2025 Reviews received at journal 01 May, 2025 Reviews received at journal 30 Apr, 2025 Reviews received at journal 29 Apr, 2025 Reviews received at journal 29 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviews received at journal 27 Apr, 2025 Reviewers agreed at journal 26 Apr, 2025 Reviewers agreed at journal 26 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers invited by journal 24 Apr, 2025 Submission checks completed at journal 23 Apr, 2025 First submitted to journal 21 Apr, 2025 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-6048692","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448486234,"identity":"6f93aab2-1c9c-4ff3-a9c0-ae44aae8df1a","order_by":0,"name":"Agbata Benedict 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