Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models
DOI:
https://doi.org/10.31686/ijier.vol7.iss10.1841Keywords:
Forecasting, Holt-Winters, Tuberculosis, Time Series, Univariate ModelsAbstract
Tuberculosis (TB) remains the world's deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country's public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil's U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here.
References
[2] AKHTAR, S.; MOHAMMAD, H. G. Seasonality in pulmonary tuberculosis among migrant workers entering Kuwait. BMC Infectious Diseases, v. 8, n. 3, 2008.
[3] ATKINSON, R. W. et al. Fine particle components and health – a systematic review and meta-analysis of epidemiological time series studies of daily mortality and hospital admissions. J Expo Sci Environ Epidemiol. v. 25, pp. 208-214, 2015.
[4] AZEEZ, A. et al. Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model. International Journal of Environmental Research and Public Health, n. 13, p. 757, 2016.
[5] BILLAH, B. et al. Exponential Smoothing Model Selection for Forecasting. International Journal of Forecasting, n. 22, p. 239-247, 2006.
[6] BORISOV, S. E. et al. Effectiveness and safety of bedaquiline-containing regimens in the treatment of MDR- and XDR-TB: a multicentre study. Eur Respir J. v. 49, ed. 5, 2017.
[7] CAO, S. et al. A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Medical Informatics and Decision Making, v. 13, n. 56, p. 1-7, 2013.
[8] DOBRE, I.; ALEXANDRU, A. A. Modelling unemployment rate using Box-Jenkins procedure. Journal of Applied Quantitative Methods, v. 3, n. 2, p. 156-166, 2008.
[9] DRAIN, P. K. et al. Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection. Clinical Microbiology Reviews, v. 31, n. 4, p. 1-24, 2018.
[10] DRITSAKIS, N.; KLAZOGLOU, P. Time series analysis using ARIMA models: an approach to forecasting health expenditures in USA. ECONOMIA INTERNAZIONALE / INTERNATIONAL ECONOMICS , Genova (Italy), v. 72, n. 1, p. 77-106, 2019. ISSN 2499-8265.
[11] DUBE, N. Application and Comparison of Time Series Methods on Tuberculosis Incidence Data: A case study of Zimbabwe 1990-2013. Faculty of Texas Tech University, 2015.
[12] FALZON, D. et al. World Health Organization treatment guidelines for drug-resistant tuberculosis, 2016 update. Eur Respir J. v. 49, ed. 3, 2017.
[13] HOLT, C. C. Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, v. 20, p. 5-10, 2004.
[14] JERE, S.; KASENSE, B.; CHILYABANYAMA, O. Forecasting Foreign Direct Investment to Zambia: A Time Series Analysis. Open Journal of Statistics, v. 7, p. 122-131, Fvereiro 2017. <https://doi.org/10.4236/ojs.2017.71010>.
[15] KHALIQ, A.; BATOOL, S. A.; CHAUDHRY, M. N. Seasonality and trend analysis of tuberculosis in Lahore, Pakistan from 2006 to 2013. Journal of Epidemiology and Global Health, n. 5, p. 397 403, 2015.
[16] KILICMAN, A.; ROSLAN, U. Tuberculosis in the Terengganu region: Forecast and data analysis. Science Asia, v. 35, p. 392–395, 2009.
[17] MAO, Q. et al. Forecasting the Incidence of Tuberculosis in China Using the Seasonal Auto-regressive Integrated Moving Average (SARIMA) Model. Journal of Infection and Public Health, n. 11, p. 707-712, 2018.
[18] MERKER, M. et al. Transmission of Extensively Drug-Resistant Tuberculosis in South África. Nature Genetics. v. 47, 2015.
[19] MINISTRY OF HEALTH. Tuberculosis Free Brazil: National Plan to End Tuberculosis as a Public Health Problem. Health Surveillance Secretariat, Communicable Disease Surveillance Department - Brasilia, 2017.
[20] MOHAMMED, S. H. et al. Seasonal Behavior and Forecasting Trends of Tuberculosis Incidence in Holy Kerbala, Iraq. International Journal of Mycobacteriology, v. 7, n. 4, p. 361-367, 2018.
[21] NASEHI, M. et al. Forecasting tuberculosis incidence in iran using box-jenkins models. Iran Red Crescent Med J, v. 5, n. 16, p. 1-6, 2014.
[22] NEWAZ, M. K. Comparing the Performance of Time Series Models for Forecasting Exchange Rate. BRAC University Journal, v. 5, n. 2, pp. 55-65, 2008.
[23] PAULINO, J. S. et al. Predictive Models and Health Sciences: A Brief Analysis. International Archives Of Medicine, v. 10, 2017. <http://imedicalsociety.org/ojs/index.php/iam/article/view/2271>.
[24] PIETERSEN, E. et al. Long-term outcomes of patients with extensively drug-resistant tuberculosis in South Africa: a cohort study. The Lancet, v. 383, 5–11, April, 2014.
[25] RIBEIRO, R. C. M. et al. Holt-Winters Forecasting for Brazilian Natural Gas Production. International Journal for Innovation Education and Research, v. 7, n. 6, p. 119-129, 2019.
[26] SILUYELE, I.; JERE, S. Using Box-Jenkins Models to Forecast Mobile Cellular. Open Journal of Statistics, v. 6, p. 303-309, 2016.
[27] STEIN, C. M. et al. Resistance and Susceptibility to Mycobacterium tuberculosis Infection and Disease in Tuberculosis Households in Kampala, Uganda. American Journal of Epidemiology, v. 7, n. 187, p. 1477–1489, 2018.
[28] TAHERI, S.; MAMMADOV, M. Learning the Naive Bayes classifier with optimization models. International Journal of Applied Mathematics and Computer Science, v. 23, n. 4, p. 787–795, 2013. <https://doi.org/10.2478/amcs-2013-0059>
[29] TULARAM, G. A.; SAEED, T. Oil-Price Forecasting Based on Various Univariate Time-Series Models. American Journal of Operations Research, v. 6, p. 226-235, 2016. <http://dx.doi.org/10.4236/ajor.2016.63023>.
[30] WINTERS, P. R. Forecasting sales by exponentially weighted moving averages. Management Science, v. 6, p. 324– 342, 1960.
[31] WORLD HEALTH ORGANIZATION. Global Tuberculosis Report. World Health Organization. Geneva. 2018.
[32] WUBULI, A. et al. Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China. PLoS ONE, v. 12, n. 7, p. 1-12, 2017.
[34] ZHENG, Y. et al. Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China. PloS ONE, v. 10, n. 3, p. 1-13, 2015.
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Copyright (c) 2019 Rhuan Carlos Martins Ribeiro, Thaynara Araújo Quadros, John Jairo Saldarriaga Ausique, Otavio Andre Chase, Pedro Silvestre da Silva Campos, Paulo Cerqueira dos Santos Júnior, José Felipe Souza de Almeida, Glauber Tadaiesky Marques
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