Research Article

Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model

Volume: 4 Number: 3 September 30, 2024
EN

Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model

Abstract

Tuberculosis (TB) remains a formidable global health challenge, demanding effective control strategies to alleviate its burden. In this study, we introduce a comprehensive mathematical model to unravel the intricate dynamics of TB transmission and assess the efficacy and cost-effectiveness of diverse intervention strategies. Our model meticulously categorizes the total population into seven distinct compartments, encompassing susceptibility, vaccination, diagnosed infectious, undiagnosed infectious, hospitalized, and recovered individuals. Factors such as susceptible individual recruitment, the impact of vaccination, immunity loss, and the nuanced dynamics of transmission between compartments are considered. Notably, we compute the basic reproduction number, providing a quantitative measure of TB transmission potential. Through this comprehensive model, our study aims to offer valuable insights into optimal control measures for TB prevention and control, contributing to the ongoing global efforts to combat this pressing health challenge.

Keywords

Tuberculosis, basic reproduction number, drug resistance, preventive strategies

References

  1. [1] World Health Organization, Global Tuberculosis Report 2021, (2021). https://www.who.int/teams/global-tuberculosis-programme/tb-reports/ global-tuberculosis-report-2021
  2. [2] World Health Organization, Global Tuberculosis Report 2022, (2022). https://www.who.int/teams/global-tuberculosis-programme/tb-reports/ global-tuberculosis-report-2022
  3. [3] World Health Organization, Latent Tuberculosis Infection: Updated and Consolidated Guidelines for Programmatic Management, (2023). https://www.who.int/tb/publications/201
  4. [4] World Health Organization, The END TB Strategy, (2015). https://www.who.int/ publications/i/item/WHO-HTM-TB-2015.19
  5. [5] Zumla, A., Raviglione, M., Hafner, R. and Von Reyn, C.F. Tuberculosis. The New England Journal of Medicine, 368(8), 745-755, (2013).
  6. [6] Dodd, P.J., Sismanidis, C. and Seddon, J.A. Global burden of drug-resistant tuberculosis in children: a mathematical modelling study. The Lancet Infectious Diseases, 16(10), 1193-1201, (2016).
  7. [7] Centers for Disease Control and Prevention, Tuberculosis (TB)-Data and Statistics, (2023). https://www.cdc.gov/tb/statistics/default.htm
  8. [8] Gupta, R.K., Lipman, M., Story, A., Hayward, A., De Vries, G., Van Hest, R. et al. Active case finding and treatment adherence in risk groups in the tuberculosis pre-elimination era. The International Journal of Tuberculosis and Lung Disease, 22(5), 479-487, (2018).
  9. [9] Goufo, E.F.D., Maritz, R. and Pene, M.K. A mathematical and ecological analysis of the effects of petroleum oil droplets breaking up and spreading in aquatic environments. International Journal of Environment and Pollution, 61(1), 64-71, (2017).
  10. [10] Atangana, A. and Doungmo Goufo, E.F. Computational analysis of the model describing HIV infection of CD4+ T cells. BioMed Research International, 2014, 618404, (2014).
APA
Peter, O. J., Abidemi, A., Fatmawati, F., Ojo, M. M., & Oguntolu, F. A. (2024). Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model. Mathematical Modelling and Numerical Simulation With Applications, 4(3), 238-255. https://doi.org/10.53391/mmnsa.1461011
AMA
1.Peter OJ, Abidemi A, Fatmawati F, Ojo MM, Oguntolu FA. Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model. MMNSA. 2024;4(3):238-255. doi:10.53391/mmnsa.1461011
Chicago
Peter, Olumuyiwa James, Afeez Abidemi, Fatmawati Fatmawati, Mayowa M. Ojo, and Festus Abiodun Oguntolu. 2024. “Optimizing Tuberculosis Control: A Comprehensive Simulation of Integrated Interventions Using a Mathematical Model”. Mathematical Modelling and Numerical Simulation With Applications 4 (3): 238-55. https://doi.org/10.53391/mmnsa.1461011.
EndNote
Peter OJ, Abidemi A, Fatmawati F, Ojo MM, Oguntolu FA (September 1, 2024) Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model. Mathematical Modelling and Numerical Simulation with Applications 4 3 238–255.
IEEE
[1]O. J. Peter, A. Abidemi, F. Fatmawati, M. M. Ojo, and F. A. Oguntolu, “Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model”, MMNSA, vol. 4, no. 3, pp. 238–255, Sept. 2024, doi: 10.53391/mmnsa.1461011.
ISNAD
Peter, Olumuyiwa James - Abidemi, Afeez - Fatmawati, Fatmawati - Ojo, Mayowa M. - Oguntolu, Festus Abiodun. “Optimizing Tuberculosis Control: A Comprehensive Simulation of Integrated Interventions Using a Mathematical Model”. Mathematical Modelling and Numerical Simulation with Applications 4/3 (September 1, 2024): 238-255. https://doi.org/10.53391/mmnsa.1461011.
JAMA
1.Peter OJ, Abidemi A, Fatmawati F, Ojo MM, Oguntolu FA. Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model. MMNSA. 2024;4:238–255.
MLA
Peter, Olumuyiwa James, et al. “Optimizing Tuberculosis Control: A Comprehensive Simulation of Integrated Interventions Using a Mathematical Model”. Mathematical Modelling and Numerical Simulation With Applications, vol. 4, no. 3, Sept. 2024, pp. 238-55, doi:10.53391/mmnsa.1461011.
Vancouver
1.Olumuyiwa James Peter, Afeez Abidemi, Fatmawati Fatmawati, Mayowa M. Ojo, Festus Abiodun Oguntolu. Optimizing tuberculosis control: a comprehensive simulation of integrated interventions using a mathematical model. MMNSA. 2024 Sep. 1;4(3):238-55. doi:10.53391/mmnsa.1461011