Seasonality and Trend Forecasting of Tuberculosis Incidence in Chongqing, China

Abstract

Tuberculosis (TB) is a global infectious disease and one of the ten leading causes of death worldwide. As TB incidence is seasonal, a reliable forecasting model that incorporates both seasonal and trend effects would be useful to improve the prevention and control of TB. In this study, the X12 autoregressive integrated moving average (X12-ARIMA) model was constructed by dividing the sequence into season term and trend term to forecast the two terms, respectively. Data regarding the TB report rate from January 2004 to December 2015 were included in the model, and the TB report data from January 2016 to December 2016 were used to validate the results. The X12-ARIMA model was compared with the seasonal autoregressive integrated moving average (SARIMA) model. A total of 383,797 cases were reported from January 2004 to December 2016 in Chongqing, China. The report rate of TB was highest in 2005 (151.06 per 100,000 population) and lowest in 2016 (72.58 per 100,000 population). The final X12-ARIMA model included the ARIMA (3,1,3) model for the trend term and the ARIMA (2,1,3) model for the season term. The SARIMA (1,0,2) * (1,1,1)₁₂ model was selected for the SARIMA model. The mean absolute error (MAE) and mean absolute percentage error (MAPE) of fitting and predicting performance based on the X12-ARIMA model were less than the SARIMA model. In conclusion, the occurrence of TB in Chongqing is controlled, which may be attributed to socioeconomic developments and improved TB prevention and control services. Applying the X12-ARIMA model is an effective method to forecast and analyze the trend and seasonality of TB.

Keywords: Prediction model; Seasonality; Tuberculosis control; Tuberculosis incidence.