Application of exponential smoothing method and ARIMA model to predicting pulmonary tuberculosis in students
ZHOU Mei-yan1, HUANG Ying2, YAN Yu-long1, ZHOU Jie1, HUANG Bo1
1. School of Public Health, University of South China, Hengyang, Hunan 421001, China; 2. Hengyang Municipal Center for Disease Control and Prevention, Hengyang, Hunan 421001, China
Abstract：Objective To explore the feasibility of exponential smoothing method and auto regressive integrated moving average (ARIMA) model in the prediction of pulmonary tuberculosis epidemic among students in Hengyang City, to compare the prediction effects of the two models and then determine a better model so as to provide references for early detection and scientific control of tuberculosis epidemic in schools. Methods We collected the data concerning pulmonary tuberculosis among students in Hengyang City from 2010 to 2020, and then compared the fit and advantages and disadvantages of prediction effects of the two models. Results The best fitting exponential smoothing model was the Holt-Winter additive model. The fitted R2, stationary R2, root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and normalized Bayesian information criterion (BIC) were 0.666, 0.469, 5.716, 31.276, 3.873 and 3.606, respectively. Ljung-Box Q was 20.741, P 0.145, and the average relative error of prediction from January to December 2020 verified 39.98%. The best fitting ARIMA model was ARIMA(0,0,1)×(0,1,1)12 model, and the fitted stationary R2, R2, RMSE, MAPE, MAE and normalized BIC were 0.500, 0.603, 6.532, 34.623, 4.443 and 3.885, respectively. The average relative error of prediction from January to December 2020 verified was 120.76%. Conclusion Compared with the ARIMA model, the exponential smoothing model is more effective in fitting the number of pulmonary tuberculosis cases among students in Hengyang City; moreover, its prediction accuracy is higher.
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2022, Vol. 29 
