Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables
China has the second largest number of tuberculosis (TB) cases in the world, and the Xinjiang province has the highest TB incidence in China. Urumqi is the capital city of Xinjiang; good TB prevention and control in Urumqi can provide an example for other parts of Xinjiang, considering that predicti...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3619063 |
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| author | Yanling Zheng |
| author_facet | Yanling Zheng |
| author_sort | Yanling Zheng |
| collection | DOAJ |
| description | China has the second largest number of tuberculosis (TB) cases in the world, and the Xinjiang province has the highest TB incidence in China. Urumqi is the capital city of Xinjiang; good TB prevention and control in Urumqi can provide an example for other parts of Xinjiang, considering that predicting the TB incidence is the prerequisite of prevention and control; therefore, it is necessary to do a prediction study on TB incidence in Urumqi. In this paper, based on the data of TB incidence and air pollution variables (PM2.5, PM10, SO2, CO, NO2, O3) in Urumqi, the ARMA (1, (1, 3)) + model was established by time series ARMA model method, cross-correlation analysis, and principal component regression method, and its predictive performance was superior to that of the ARMA (1, (1, 3)) model based on TB historical data. The research idea of this paper was good, which can provide a reference for other researchers. The prediction of the ARMA (1, (1, 3)) + model can provide scientific help for TB prevention and control in Urumqi, China. During the analysis, it was found that the higher the concentration of O3, the higher the incidence of TB. This study suggests that people in Urumqi should pay more attention to the hazards of O3 and do a good job of personal protection. |
| format | Article |
| id | doaj-art-18e8237d722240d7bdeefe84bcca13e4 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-18e8237d722240d7bdeefe84bcca13e42025-08-20T02:02:13ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/36190633619063Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution VariablesYanling Zheng0College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, ChinaChina has the second largest number of tuberculosis (TB) cases in the world, and the Xinjiang province has the highest TB incidence in China. Urumqi is the capital city of Xinjiang; good TB prevention and control in Urumqi can provide an example for other parts of Xinjiang, considering that predicting the TB incidence is the prerequisite of prevention and control; therefore, it is necessary to do a prediction study on TB incidence in Urumqi. In this paper, based on the data of TB incidence and air pollution variables (PM2.5, PM10, SO2, CO, NO2, O3) in Urumqi, the ARMA (1, (1, 3)) + model was established by time series ARMA model method, cross-correlation analysis, and principal component regression method, and its predictive performance was superior to that of the ARMA (1, (1, 3)) model based on TB historical data. The research idea of this paper was good, which can provide a reference for other researchers. The prediction of the ARMA (1, (1, 3)) + model can provide scientific help for TB prevention and control in Urumqi, China. During the analysis, it was found that the higher the concentration of O3, the higher the incidence of TB. This study suggests that people in Urumqi should pay more attention to the hazards of O3 and do a good job of personal protection.http://dx.doi.org/10.1155/2020/3619063 |
| spellingShingle | Yanling Zheng Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables Complexity |
| title | Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables |
| title_full | Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables |
| title_fullStr | Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables |
| title_full_unstemmed | Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables |
| title_short | Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables |
| title_sort | predictive study of tuberculosis incidence by arma model combined with air pollution variables |
| url | http://dx.doi.org/10.1155/2020/3619063 |
| work_keys_str_mv | AT yanlingzheng predictivestudyoftuberculosisincidencebyarmamodelcombinedwithairpollutionvariables |