Forecasting Analysis for Tuberculosis (TB) Incidence in Tamilnadu

S. Poyyamozhi¹ , A. Kachi Mohideen²

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.6 , pp.321-327, Dec-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v5i6.321327

Online published on Dec 31, 2018

Copyright © S. Poyyamozhi, A. Kachi Mohideen . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

249 Views    133 Downloads    133 Downloads

Abstract :
Tuberculosis (TB) remains a major global public health problem, especially for considered as high burden cities in Tamilnadu. It is considered by WHO as one of the high burden countries and tuberculosis incidence continues to be very high. Therefore, there is need to continue monitoring and predicting tuberculosis incidence in an effort to make the control of tuberculosis more effective. The Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the incidence of infectious diseases. This method takes into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. Holt Winters (HW) methods also play a significant role in time series forecasting and are especially effective for short term forecasting. In this study, based on the data of the tuberculosis incidence from 2005 -2017 in Tamilnadu, we establish the single ARIMA (2, 2, 1) model, the combined ARIMA (2, 2, 1)-ARCH (1) model, and the HW model, which can be used to predict the tuberculosis incidence successfully in Tamilnadu. Comparative analyses show that the ARIMA and ARIMA-ARCH models perform reasonably well, with the ARIMA model being the best in our case. To the best of our knowledge, this is the First study to establish the ARIMA model and ARIMA-ARCH model for pre-diction and monitoring the yearly incidence of tuberculosis (TB) in Tamilnadu. Based on the results of this study, the ARIMA (2, 2, 1) and ARIMA (2, 2, 1)-ARCH (1) models are suggested to give tuberculosis surveillance by providing estimates on tuberculosis incidence trends in Tamilnadu.

Key-Words / Index Term :
Tuberculosis, Box-Jenkins approach, the autoregressive integrated moving average (ARIMA) model, Heteroscedasticity (ARCH)model, Holt Winters (HW) method

References :
[1]. Cain, K.P., Oeltmann, J.E., Kammer er, J.S., Moonan, P.K., Ricks, P.M (2011). Estimating the burden of tuberculosis among foreign-born persons acquired prior to entering the u.s., 2005-2009. PLoS One, 6:e27405.
[2]. Floyd, K., Raviglione, M., Glaziou, P.(2009) Global burden and epidemiology of tuberculosis. Clin. Chest Med., 30:621636.
[3]. World Health Organization(2017). Who, global tuberculosis report.
[4]. Sapii, N., Dir, S., Mardiah, T., Abdullah, S(2012). Aplication of univariate forecasting models of tuberculosis cases in kelantan. ICSSBE.
[5]. Roslanb, U., Kilicmana, A. Tuberculosis in the terengganu region. Forecast and data analysis. Scie Asia.
[6]. Willis MD, Winston CA, Heilig CM, Cain KP, Walter ND, Mac Kenzie WR. Seasonality of tuberculosis in the United States, 1993-2008.Clin Infect Dis. 2 012;54(11):1553-60.
[7]. Zhang Y, Tang G, Wang W. Application of ARIMA Model in forecasting incidence of tuberculosis. Modern Prevent Med. 2008;9:6.
[8]. Abdullah S, Sapii N, Dir S, Mardiah T. (2012) Aplication of univariate forecasting models of tuberculosis cases in Kelantan, in Statistics in Science, Business, and Engineering (ICSSBE). 2012 International Conference on: IEEE. Langkawi, Kedah, Malaysia.
[9]. Engle, R.F. Autoregressive conditional heteroscedasticity with estimates of variance ofed kingdom ination. Econometrica.
[10]. Roslanb, U., Kilicmana, A. Tuberculosis in the terengganu region. Forecast and data analysis. Scie Asia.
[11]. Zhang, L.P., Zhang, X.L., Wang, K., Zheng, Y.J., Zheng, Y.L.(2015), Forecast model analysis for the morbidity of tuberculosis in Xinjiang,China. PLoS ONE, 10(3).
[12]. Liu, X., Jiang, B., Yang, W., Liu, Q. Forecasting incidence of hemorrhagic fever with renal syndrome in china using arima model. BMC Infect Dis.
[13]. Tong, L.I., Lee, Y.S. Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowl-Based. Syst.
[14]. Central TB Division in India report Directorate General of Health Services, Ministry of Health and Family Welfare, Nirman Bhavan, New Delhi 10011(http://www.tbcindia.org)
[15]. Tuberculosis Control in the South-East Asia Region Annual Report 2013.
[16]. WHO Library Cataloguing in Publication Data Assessing tuberculosis revalence through population-based surveys, (2007).