| Summary: | Qiao Liu,1,2,* Zhongqi Li,1,* Ye Ji,1 Leonardo Martinez,3 Ui Haq Zia,4 Arshad Javaid,4 Wei Lu,2 Jianming Wang1,51Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, People’s Republic of China; 3Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA; 4Faculty of Public Health and Social Sciences, Khyber Medical University, Peshawar, Pakistan; 5Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China*These authors contributed equally to this workObjective: Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China.Methods: We applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect.Results: During 2005–2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1)12 and BPNN (3-9-1) were defined as the optimal fitting models. In the fitting dataset, the RMSE, MAPE, MAE and MER were 0.3901, 6.0498, 0.2740 and 0.0608, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)12 model, 0.3236, 6.0113, 0.2508 and 0.0587, respectively, for the BPNN model. In the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.1758, 4.6041, 0.1368 and 0.0444, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)12 model, and 0.1382, 3.2172, 0.1018 and 0.0330, respectively, for the BPNN model.Conclusion: Both the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.Keywords: ARIMA, BPNN, tuberculosis, incidence, forecasting
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