Abstract：Objective To construct a seasonal ARIMA model, to explore the impact of coronavirus disease 2019 (COVID-19) on the epidemic characteristics of tuberculosis, and to predict the incidence trend of tuberculosis in Baoshan District, Shanghai. Methods Data about the monthly incidence rate of tuberculosis in Baoshan District of Shanghai from 2009 to 2021 were collected, and a seasonal ARIMA model was constructed to verify the effect of the prediction model, and the causes of the prediction error were analyzed. Results The monthly incidence model of tuberculosis in Baoshan District was ARIMA (2,0,0)(0,1,1)12, with the lowest BIC, Ljung-Box statistic Q=23.127, P=0.081, and the residual order was white noise. The actual monthly incidence rates in 2019 were basically consistent with the predicted values, and all were within the 95% confidence interval of the predicted value. Due to the impact of the COVID-19 pandemic, there was a large difference between the observed values and the predicted values in the past two years, and the observed values in February 2021 were outside the 95% confidence interval of the fitted values. Conclusion ARIMA (2,0,0)(0,1,1)12 model can accurately predict the incidence trend of tuberculosisbefore the COVID-19 pandemic in Baoshan District.However, when affected by the COVID-19 pandemic, the prediction results have a large deviation, which requires re-modeling of tuberculosis incidence data in the post-epidemic era.
乐博昕, 刘效峰, 王娜, 胡伟宏, 冯太聪, 蔡博. 基于ARIMA的新冠肺炎疫情前后结核病流行趋势预测与分析[J]. 实用预防医学, 2022, 29(11): 1299-1302.
LE Bo-xin, LIU Xiao-feng, WANG Na, HU Wei-hong, FENG Tai-cong, CAI Bo. Prediction and analysis of epidemic trend of tuberculosis before and after the COVID-19 pandemic based on ARIMA. , 2022, 29(11): 1299-1302.
2022, Vol. 29 
