International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

TB Program Success Story in Togo: Evidence from the Multilayer Peceptron Neural Network

Dr. Smartson. P. NYONIZICHIRe Project, University of Zimbabwe, Harare, ZimbabweThabani NYONIDepartment of Economics, University of Zimbabwe, Harare, Zimbabwe

Vol 5 No 3 (2021): Volume 5, Issue 3, March 2021 | Pages: 358-362

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 01-04-2021

doi Logo doi.org/10.47001/IRJIET/2021.503061

pdf iconFull Text PDF
Abstract
Modeling and forecasting TB incidence is necessary in order to inform policy and stimulate an appropriate national response to the epidemic. In this research article, the ANN approach was applied to analyze TB incidence in Togo. The employed annual data covers the period 2000-2018 and the out-of-sample period 2019-2023. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied model indicate that the model is stable in forecasting TB incidence in Togo. The model predicts that the incidence of TB will low, close to 30 cases per 100 000/year over the period 2019-2023.The government is encouraged to continue on this desirable path by strengthening TB/HIV collaboration.
Keywords

ANN, Forecasting, TB incidence.


Citation of this Article

Dr. Smartson. P. NYONI, Thabani NYONI, “TB Program Success Story in Togo: Evidence from the Multilayer Peceptron Neural Network” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 3, pp 358-362, March 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.503061

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Lv L., Li T., &Xu K (2018). Sputum bacteriology conversion and treatment outcome of patients with multidrug-resistant tuberculosis. Infect Drug Resist; 11:147–154.
  2. Mao Q., Zhang K., Yan W & Cheng C (2018). Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. J Infect Public Health. 2018;11 (5):707–712.
  3. Selain Kasereka Kabunga., Emile F., Doungmo Goufo & Vinh Ho Tuong (2020). Analysis and simulation of a mathematical model of tuberculosis transmission in Democratic Republic of the Congo. Advances in Dierence Equations 2020:642
  4. Sgaragli G& Frosini M (2016). Human tuberculosis I. Epidemiology, diagnosis and pathogenetic mechanisms. Curr Med Chem; 23 (25):2836–2873.
  5. Singh R., Dwivedi SP., Gaharwar US., Meena R., Rajamani P& Prasad T (2019). Recent updates on drug resistance in Mycobacterium tuberculosis. J Appl Microbiol;1–21.
  6. Wang H., Tian CW., Wang WM & Luo XM (2018). Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiol Infect.; 146 (8):935–939.
  7. WHO (2014). The End TB Strategy. 2014. Available from: https://wwwwhoint/ tb/End_TB_brochure pdf.
  8. WHO (2017). Global tuberculosis report 2017. Available at http://www.who.int/ tb/publications/global report/en/.
  9. WHO (2018). Global Tuberculosis report 2018.Geneva, Switzerland
  10. WHO (2019). Global strategy and targets for tuberculosis prevention, care and control after 2015. Available from: https://wwwwhoint/tb/ post2015_strategy/en/.
  11. WHO (2019). Global tuberculosis report 2019.Geneva, Switzerland. 

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.