Wang Y, Xu C, Ren J, et al. - Considering the utility of creating a long-term forecast for tuberculosis (TB) for better launching prevention initiatives and a lack of such a forecasting method with robust and accurate performance, researchers here examined its potential of the error-trend-seasonal (ETS) framework by a series of comparative experiments to investigate and forecast its secular epidemic seasonality and trends of TB incidence in China. The TB incidence data were collected from January 1997 to August 2019, and then the data were partitioned into eight different training and testing subsamples. Thereafter, the ETS and development of seasonal autoregressive integrated moving average (SARIMA) models were constructed based on the training subsamples; multiple performance indices were adopted including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate to assess their simulation and projection effects. The analysis suggests that the ETS framework has the ability to direct long-term forecasting for TB incidence, which may be helpful for the long-term planning of TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, they recommend the formulation of more particular strategies to dramatically expedite progress towards the goals of the End TB Strategy.