Application of SARIMA model and Holt winters index smoothing method to predict incidence of pulmonary tuberculosis in Jiangsu

  Objective   To establish a seasonal auto regressive integrated moving average (SARIMA) model and a Holt-Winters exponential smoothing model for the prediction of the case number of tuberculosis (TB) in Jiangsu province and provide scientific reference for the prevention and control of TB in Jiangsu.

  Methods   The SARIMA model and Holt-Winters exponential smoothing model were established by using the TB incidence data in Jiangsu from January 2016 to December 2020. The validation of the model used the TB incidence from January to December 2021 and evaluation of the models’ prediction effect used root-mean-square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

  Results   The best SARIMA model was (0,1,2) (0,1,0) ₁₂, the RMSE was 229.52, MAE was 146.81 and MAPE was 6.33%, and the total relative error was 5.21%. For Holt winters additive model, the RMSE was 206.75, MAE was 156.45, MAPE was 6.63%, and the total relative error was 7.74%.

  Conclusion   Both models can well fit the number of pulmonary TB, and the performance of SARIMA model was slightly better.