Forecasting TB Incidence in Yemen Using the Multilayer Perceptron Neural Network

Abstract

In this research paper, the ANN approach was applied to analyze TB incidence in Yemen. The employed data covers the period 2000-2018 and the out-of-sample period ranges over the 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 Yemen. The results of the study indicate that TB incidence will be around 48 cases/100 000 /year over the period 2019-2023. In order to contribute meaningfully to the national control strategy of a TB-free Yemen, authorities should, among other things, intensify TB surveillance and control programmes in order to reduce incidence to below 30 cases per 100 000/year.  

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Thabani NYONI

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. Department of Economics, University of Zimbabwe, Harare, Zimbabwe

IRJIET, Volume 5, Issue 3, March 2021 pp. 316-320

doi.org/10.47001/IRJIET/2021.503054

References

  1. Anokye R., Acheampong E., & Owusu (2018). Time series analysis of malaria in Kumasi: Using ARIMA models to forecast future incidence. Cogent Soc Sci. ;4(1): 1-9.
  2. Azeez A., Obaromi D., Odeyemi A., Ndege J & Muntabayi R (2016). Seasonality and trend forecasting of tuberculosis prevalence data in Eastern Cape, South Africa, using a hybrid model. Int J Environ Res Public Health;13. pii: E757.
  3. Box GEP, Jenkins GM & Reinsel (2015). Time series analysis: forecasting and control, 5th edition. J Oper Res Soc; 22(2): 199–201.
  4. Box., George E.P., Jenkins., Gwilym M, Reinsel & Gregory C (2010). Time series analysis. Forecasting and control. 3rd ed. journal of time. 2010; 31(4):303 - 309.
  5. Carvajal Thaddeus M., Viacrusis Katherine M & Hernandez Lara Fides T (2018). Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines. BMC Infect Dis. 2018; 18: 183-189.
  6. Khaliq A., Batool SA., &Chaudhry MN (2015). Seasonality and trend analysis of tuberculosis in Lahore, Pakistan from 2006 to 2013. J Epidemiol Glob Health 5: 397-403.
  7. Liao Z., Zhang X &Zhang Y (2019). Seasonality and Trend Forecasting of Tuberculosis Incidence in Chongqing, China. Inter discip Sci; 11(1): 77–85.
  8. Mao Q., Zhang K &Yan W (2018). Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. J Infect Public Health;11(5): 707–12.
  9. Naranbat N., Nymadawa P., Schopfer K & Rieder HL (2009). Seasonality of tuberculosis in an Eastern Asian country with an extreme continental climate. Eur Respir J, 34:921-925.
  10. Siregar FA., Makmur T & Saprin S (2018). Forecasting dengue hemorrhagic fever cases using ARIMA model: a case study in Asahan district. In: IOP Conference Series Materials Science and Engineering; p. 300.
  11. Tohidinik HR., Mohebali M., Mansournia MA (2018). Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis. Tropical Med Int Health, 23(8): 860–9.
  12. Wang H., Tian CW., Wang WM (2018). Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiol Infect. 2018;146(8):935–9.
  13. WHO (2016). Global Tuberculosis Report 2016.Geneva, Switzerland. 
  14. Willis MD., Winston CA., Heilig CM., Cain KP., Walter ND & Mac Kenzie WR (2012). Seasonality of tuberculosis in the United States, 1993-2008. Clin Infect Dis; 54:1553-1560.
  15. Withanage GP., Viswakula SD., Yi SGN (2018). A forecasting model for dengue incidence in the district of Gampaha, Sri Lanka.
  16. Wubuli A., Li Y., Xue F., Yao X., Upur H &Wushouer Q (2017). Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China. PLoS One, 12: e0180226.
  17. Xu Q., Li R., Liu Y (2017). Forecasting the incidence of mumps in Zibo City based on a SARIMA model. Int J Environ Res Public Health;14(8):925 937.