Abstract:

Objective: To analysis the trend of monthly reported deaths of pulmonary tuberculosis in China, to establish and determine the optimal autoregressive integrated moving average model (ARIMA). Methods: Monthly reported deaths of pulmonary tuberculosis in China (excluding Hong Kong, Macao and Taiwan) reported on Disease Surveillance from 2010 to 2020 was collected, and the total number was 21055.Date from 2010 to 2018 were used as modeling databases to form the time series and fit the ARIMA model. The actual date monthly reported in 2019 and 2020 were used as verification database to screen and evaluate the ARIMA models, then the optimal model was selected and used to predict monthly reported deaths of pulmonary tuberculosis from January 2021 to December 2021 in China. Results: The model was constructed based on the number of monthly reported deaths of pulmonary tuberculosis in China from 2010 to 2018, and three alternative models were preliminarily screened out through parameter evaluation and overall diagnosis; they were ARIMA (0,1,1) (1,1,0)₁₂ with the maximum stationary R-square (R ²=0.589), ARIMA(0,1,2) with the minimum root mean squared error (RMSE=24.572) and ARIMA (0,1,1) with the minimum value of standardized Bayesian information criterion (NBIC=6.517), respectively. The alternative model was used to predict the number of monthly reported deaths of pulmonary tuberculosis in China from 2019 to 2020. Compared with the actual data, ARIMA (0,1,1) was the optimal prediction model, and the relative errors of the predicted data in 2019 and 2020 were 6.56% (147/2241) and 58.52% (910/1555), respectively. ARIMA (0,1,1) model was used to predict the monthly reported deaths of pulmonary tuberculosis in China, the total number from January 2021 to December 2021 would be about 2542, with average of 212 cases monthly. Conclusion: ARIMA model had a good short-term prediction effect and could be used to predict the number of monthly reported deaths of pulmonary tuberculosis in China; however, it was not effective in predicting long-term or year date affected by some large factors.

Key words: Tuberculosis,pulmonary, Death, Models,statistical, Forecasting