IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i5p1104-d1077273.html
   My bibliography  Save this article

Analysis of the In-Host Dynamics of Tuberculosis and SARS-CoV-2 Coinfection

Author

Listed:
  • Ahmed M. Elaiw

    (Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia)

  • Afnan D. Al Agha

    (Department of Mathematical Science, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia)

Abstract

The coronavirus disease 2019 (COVID-19) is a respiratory disease that appeared in 2019 caused by a virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is still spreading and causing deaths around the world. There is a real concern of SARS-CoV-2 coinfection with other infectious diseases. Tuberculosis (TB) is a bacterial disease caused by Mycobacterium tuberculosis (Mtb). SARS-CoV-2 coinfection with TB has been recorded in many countries. It has been suggested that the coinfection is associated with severe disease and death. Mathematical modeling is an effective tool that can help understand the dynamics of coinfection between new diseases and well-known diseases. In this paper, we develop an in-host TB and SARS-CoV-2 coinfection model with cytotoxic T lymphocytes (CTLs). The model investigates the interactions between healthy epithelial cells (ECs), latent Mtb-infected ECs, active Mtb-infected ECs, SARS-CoV-2-infected ECs, free Mtb, free SARS-CoV-2, and CTLs. The model’s solutions are proved to be nonnegative and bounded. All equilibria with their existence conditions are calculated. Proper Lyapunov functions are selected to examine the global stability of equilibria. Numerical simulations are implemented to verify the theoretical results. It is found that the model has six equilibrium points. These points reflect two states: the mono-infection state where SARS-CoV-2 or TB occurs as a single infection, and the coinfection state where the two infections occur simultaneously. The parameters that control the movement between these states should be tested in order to develop better treatments for TB and COVID-19 coinfected patients. Lymphopenia increases the concentration of SARS-CoV-2 particles and thus can worsen the health status of the coinfected patient.

Suggested Citation

  • Ahmed M. Elaiw & Afnan D. Al Agha, 2023. "Analysis of the In-Host Dynamics of Tuberculosis and SARS-CoV-2 Coinfection," Mathematics, MDPI, vol. 11(5), pages 1-24, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1104-:d:1077273
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/5/1104/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/5/1104/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yimin Du & Jianhong Wu & Jane M. Heffernan, 2017. "A simple in-host model for Mycobacterium tuberculosis that captures all infection outcomes," Mathematical Population Studies, Taylor & Francis Journals, vol. 24(1), pages 37-63, January.
    2. Bandekar, Shraddha Ramdas & Ghosh, Mini, 2022. "A co-infection model on TB - COVID-19 with optimal control and sensitivity analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 1-31.
    3. Kassahun Getnet Mekonen & Shiferaw Feyissa Balcha & Legesse Lemecha Obsu & Abdulkadir Hassen, 2022. "Mathematical Modeling and Analysis of TB and COVID-19 Coinfection," Journal of Applied Mathematics, Hindawi, vol. 2022, pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ali Algarni & Afnan D. Al Agha & Aisha Fayomi & Hakim Al Garalleh, 2023. "Kinetics of a Reaction-Diffusion Mtb/SARS-CoV-2 Coinfection Model with Immunity," Mathematics, MDPI, vol. 11(7), pages 1-25, April.
    2. Ojo, Mayowa M. & Peter, Olumuyiwa James & Goufo, Emile Franc Doungmo & Nisar, Kottakkaran Sooppy, 2023. "A mathematical model for the co-dynamics of COVID-19 and tuberculosis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 499-520.
    3. Zhang, Wenjing, 2022. "Disease clearance of tuberculosis infection: An in-host continuous-time Markov chain model," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    4. Prem Kumar, R. & Santra, P.K. & Mahapatra, G.S., 2023. "Global stability and analysing the sensitivity of parameters of a multiple-susceptible population model of SARS-CoV-2 emphasising vaccination drive," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 741-766.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1104-:d:1077273. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.