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Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses.
Infect Drug Resist. 2019; 12:2311-2322.ID

Abstract

Objective

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.

Authors+Show Affiliations

Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China. Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA.Faculty of Public Health and Social Sciences, Khyber Medical University, Peshawar, Pakistan.Faculty of Public Health and Social Sciences, Khyber Medical University, Peshawar, Pakistan.Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China. Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31440067

Citation

Liu, Qiao, et al. "Forecasting the Seasonality and Trend of Pulmonary Tuberculosis in Jiangsu Province of China Using Advanced Statistical Time-series Analyses." Infection and Drug Resistance, vol. 12, 2019, pp. 2311-2322.
Liu Q, Li Z, Ji Y, et al. Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. Infect Drug Resist. 2019;12:2311-2322.
Liu, Q., Li, Z., Ji, Y., Martinez, L., Zia, U. H., Javaid, A., Lu, W., & Wang, J. (2019). Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. Infection and Drug Resistance, 12, 2311-2322. https://doi.org/10.2147/IDR.S207809
Liu Q, et al. Forecasting the Seasonality and Trend of Pulmonary Tuberculosis in Jiangsu Province of China Using Advanced Statistical Time-series Analyses. Infect Drug Resist. 2019;12:2311-2322. PubMed PMID: 31440067.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. AU - Liu,Qiao, AU - Li,Zhongqi, AU - Ji,Ye, AU - Martinez,Leonardo, AU - Zia,Ui Haq, AU - Javaid,Arshad, AU - Lu,Wei, AU - Wang,Jianming, Y1 - 2019/07/26/ PY - 2019/03/06/received PY - 2019/07/06/accepted PY - 2019/8/24/entrez PY - 2019/8/24/pubmed PY - 2019/8/24/medline KW - ARIMA KW - BPNN KW - forecasting KW - incidence KW - tuberculosis SP - 2311 EP - 2322 JF - Infection and drug resistance JO - Infect Drug Resist VL - 12 N2 - Objective: 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. SN - 1178-6973 UR - https://www.unboundmedicine.com/medline/citation/31440067/Forecasting_the_seasonality_and_trend_of_pulmonary_tuberculosis_in_Jiangsu_Province_of_China_using_advanced_statistical_time_series_analyses_ L2 - https://dx.doi.org/10.2147/IDR.S207809 DB - PRIME DP - Unbound Medicine ER -
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