Abstract:

ObjectiveTo establish an Auto-regressive Integrated Moving Average (ARIMA) model of tuberculosis (TB) in Guangzhou and use it to make prediction on the number of notified pulmonary tuberculosis (PTB) cases in Guangzhou, so as to provide a reference for the prevention and control of TB in Guangzhou. MethodsThe monthly notification incidence data of TB in Guangzhou from January 2010 to December 2019 was collected through the “Chinese Disease Prevention and Control Information System”. ARIMA model was established by conducting time series analysis to predict the notification incidence of PTB in Guangzhou in 2021. In addition, the stability of model parameters and the accuracy of prediction model were tested by re-fitting the model with data from 2010 to 2018. ResultsA total of 115887 PTB cases were notified in Guangzhou from 2010 to 2019, which showed a downward trend year by year together with a significant seasonality. The number of cases reached a peak from March to May every year, and the trough always occurred in February. The best fitting model was ARIMA(1,1,1)×(0,1,1)₁₂ (P_{residual test}=0.693, AIC=1215.300), and the mean absolute percentage error (MAPE) between the actual value and the predicted value fitted by the model for January to December 2020 was 7.29%. According to the data from 2010 to 2018, the optimal model was still ARIMA(1,1,1)×(0,1,1)₁₂ (P_{residual test}=0.847, AIC=1089.260) and MAPE between the actual value and the predicted value fitted by model for January to December in 2019 was 4.91%. The predicted number of PTB cases in Guangzhou in 2021 was 8270, and the average number of monthly cases was 689. The incidence level was slightly higher than that in 2020. Conclusion The ARIMA (1,1,1)×(0,1,1)₁₂ model made a good prediction on the number of notified PTB cases in Guangzhou, and could be used for short-term forecasting and dynamic analysis of TB in Guangzhou.

Key words: Tuberculosis,pulmonary, Disease notification, Forecasting, Models,statistical