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Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China.
BMC Infect Dis. 2020 Apr 22; 20(1):300.BI

Abstract

BACKGROUND

Tuberculosis (TB) remains a serious public health problem with substantial financial burden in China. The incidence of TB in Guangxi province is much higher than that in the national level, however, there is no predictive study of TB in recent years in Guangxi, therefore, it is urgent to construct a model to predict the incidence of TB, which could provide help for the prevention and control of TB.

METHODS

Box-Jenkins model methods have been successfully applied to predict the incidence of infectious disease. In this study, based on the analysis of TB incidence in Guangxi from January 2012 to June 2019, we constructed TB prediction model by Box-Jenkins methods, and used root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) to test the performance and prediction accuracy of model.

RESULTS

From January 2012 to June 2019, a total of 587,344 cases of TB were reported and 879 cases died in Guangxi. Based on TB incidence from January 2012 to December 2018, the SARIMA((2),0,(2))(0,1,0)12 model was established, the AIC and SC of this model were 2.87 and 2.98, the fitting accuracy indexes, such as RMSE, MAE and MAPE were 0.98, 0.77 and 5.8 respectively; the prediction accuracy indexes, such as RMSE, MAE and MAPE were 0.62, 0.45 and 3.77, respectively. Based on the SARIMA((2),0,(2))(0,1,0)12 model, we predicted the TB incidence in Guangxi from July 2019 to December 2020.

CONCLUSIONS

This study filled the gap in the prediction of TB incidence in Guangxi in recent years. The established SARIMA((2),0,(2))(0,1,0)12 model has high prediction accuracy and good prediction performance. The results suggested the change trend of TB incidence predicted by SARIMA((2),0,(2))(0,1,0)12 model from July 2019 to December 2020 was similar to that in the previous two years, and TB incidence will experience slight decrease, the predicted results can provide scientific reference for the prevention and control of TB in Guangxi, China.

Authors+Show Affiliations

College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China. zhengyl_math@sina.cn.College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32321419

Citation

Zheng, Yanling, et al. "Statistical Methods for Predicting Tuberculosis Incidence Based On Data From Guangxi, China." BMC Infectious Diseases, vol. 20, no. 1, 2020, p. 300.
Zheng Y, Zhang L, Wang L, et al. Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China. BMC Infect Dis. 2020;20(1):300.
Zheng, Y., Zhang, L., Wang, L., & Rifhat, R. (2020). Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China. BMC Infectious Diseases, 20(1), 300. https://doi.org/10.1186/s12879-020-05033-3
Zheng Y, et al. Statistical Methods for Predicting Tuberculosis Incidence Based On Data From Guangxi, China. BMC Infect Dis. 2020 Apr 22;20(1):300. PubMed PMID: 32321419.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China. AU - Zheng,Yanling, AU - Zhang,Liping, AU - Wang,Lei, AU - Rifhat,Ramziya, Y1 - 2020/04/22/ PY - 2019/09/04/received PY - 2020/04/15/accepted PY - 2020/4/24/entrez PY - 2020/4/24/pubmed PY - 2020/5/26/medline KW - Prediction KW - SARIMA model KW - Tuberculosis SP - 300 EP - 300 JF - BMC infectious diseases JO - BMC Infect. Dis. VL - 20 IS - 1 N2 - BACKGROUND: Tuberculosis (TB) remains a serious public health problem with substantial financial burden in China. The incidence of TB in Guangxi province is much higher than that in the national level, however, there is no predictive study of TB in recent years in Guangxi, therefore, it is urgent to construct a model to predict the incidence of TB, which could provide help for the prevention and control of TB. METHODS: Box-Jenkins model methods have been successfully applied to predict the incidence of infectious disease. In this study, based on the analysis of TB incidence in Guangxi from January 2012 to June 2019, we constructed TB prediction model by Box-Jenkins methods, and used root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) to test the performance and prediction accuracy of model. RESULTS: From January 2012 to June 2019, a total of 587,344 cases of TB were reported and 879 cases died in Guangxi. Based on TB incidence from January 2012 to December 2018, the SARIMA((2),0,(2))(0,1,0)12 model was established, the AIC and SC of this model were 2.87 and 2.98, the fitting accuracy indexes, such as RMSE, MAE and MAPE were 0.98, 0.77 and 5.8 respectively; the prediction accuracy indexes, such as RMSE, MAE and MAPE were 0.62, 0.45 and 3.77, respectively. Based on the SARIMA((2),0,(2))(0,1,0)12 model, we predicted the TB incidence in Guangxi from July 2019 to December 2020. CONCLUSIONS: This study filled the gap in the prediction of TB incidence in Guangxi in recent years. The established SARIMA((2),0,(2))(0,1,0)12 model has high prediction accuracy and good prediction performance. The results suggested the change trend of TB incidence predicted by SARIMA((2),0,(2))(0,1,0)12 model from July 2019 to December 2020 was similar to that in the previous two years, and TB incidence will experience slight decrease, the predicted results can provide scientific reference for the prevention and control of TB in Guangxi, China. SN - 1471-2334 UR - https://www.unboundmedicine.com/medline/citation/32321419/Statistical_methods_for_predicting_tuberculosis_incidence_based_on_data_from_Guangxi_China_ L2 - https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-020-05033-3 DB - PRIME DP - Unbound Medicine ER -