Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models

Authors

  • Rhuan Carlos Martins Ribeiro Federal Rural University of Amazon (UFRA)
  • Thaynara Araújo Quadros Univesity of Amazonia (UNAMA)
  • John Jairo Saldarriaga Ausique University of Amazonia (UNAMA) https://orcid.org/0000-0003-0388-4924 (unauthenticated)
  • Otavio Andre Chase Federal Rural University of Amazon (UFRA) https://orcid.org/0000-0003-0246-8339 (unauthenticated)
  • Pedro Silvestre da Silva Campos Federal Rural University of Amazon (UFRA)
  • Paulo Cerqueira dos Santos Júnior Federal Rural University of Amazon (UFRA)
  • José Felipe Souza de Almeida Federal Rural University of Amazon (UFRA)
  • Glauber Tadaiesky Marques Federal Rural University of Amazon (UFRA)

DOI:

https://doi.org/10.31686/ijier.vol7.iss10.1841

Keywords:

Forecasting, Holt-Winters, Tuberculosis, Time Series, Univariate Models

Abstract

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.

Downloads

Download data is not yet available.

Downloads

Published

2019-10-01

How to Cite

Ribeiro, R. C. M., Quadros, T. A., Ausique, J. J. S., Chase, O. A., Campos, P. S. da S., Júnior, P. C. dos S., Almeida, J. F. S. de, & Marques, G. T. (2019). Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models. International Journal for Innovation Education and Research, 7(10), 894-909. https://doi.org/10.31686/ijier.vol7.iss10.1841

Most read articles by the same author(s)