Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve...
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Autor*in:	Zhong-Qi Li [verfasserIn] Hong-Qiu Pan [verfasserIn] Qiao Liu [verfasserIn] Huan Song [verfasserIn] Jian-Ming Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Pulmonary tuberculosis Meteorological factor Time series Predicting

Übergeordnetes Werk:	In: Infectious Diseases of Poverty - BMC, 2013, 9(2020), 1, Seite 11
Übergeordnetes Werk:	volume:9 ; year:2020 ; number:1 ; pages:11

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s40249-020-00771-7

Katalog-ID:	DOAJ010477241

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520			\|a Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
650		4	\|a Pulmonary tuberculosis
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allfields	10.1186/s40249-020-00771-7 doi (DE-627)DOAJ010477241 (DE-599)DOAJ9eef27c70e6b4ab5aaf8fb2877c9b021 DE-627 ger DE-627 rakwb eng RC109-216 RA1-1270 Zhong-Qi Li verfasserin aut Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings. Pulmonary tuberculosis Meteorological factor Time series Predicting Infectious and parasitic diseases Public aspects of medicine Hong-Qiu Pan verfasserin aut Qiao Liu verfasserin aut Huan Song verfasserin aut Jian-Ming Wang verfasserin aut In Infectious Diseases of Poverty BMC, 2013 9(2020), 1, Seite 11 (DE-627)72939476X (DE-600)2689396-4 20499957 nnns volume:9 year:2020 number:1 pages:11 https://doi.org/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/article/9eef27c70e6b4ab5aaf8fb2877c9b021 kostenfrei http://link.springer.com/article/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/toc/2049-9957 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2020 1 11
spelling	10.1186/s40249-020-00771-7 doi (DE-627)DOAJ010477241 (DE-599)DOAJ9eef27c70e6b4ab5aaf8fb2877c9b021 DE-627 ger DE-627 rakwb eng RC109-216 RA1-1270 Zhong-Qi Li verfasserin aut Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings. Pulmonary tuberculosis Meteorological factor Time series Predicting Infectious and parasitic diseases Public aspects of medicine Hong-Qiu Pan verfasserin aut Qiao Liu verfasserin aut Huan Song verfasserin aut Jian-Ming Wang verfasserin aut In Infectious Diseases of Poverty BMC, 2013 9(2020), 1, Seite 11 (DE-627)72939476X (DE-600)2689396-4 20499957 nnns volume:9 year:2020 number:1 pages:11 https://doi.org/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/article/9eef27c70e6b4ab5aaf8fb2877c9b021 kostenfrei http://link.springer.com/article/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/toc/2049-9957 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2020 1 11
allfields_unstemmed	10.1186/s40249-020-00771-7 doi (DE-627)DOAJ010477241 (DE-599)DOAJ9eef27c70e6b4ab5aaf8fb2877c9b021 DE-627 ger DE-627 rakwb eng RC109-216 RA1-1270 Zhong-Qi Li verfasserin aut Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings. Pulmonary tuberculosis Meteorological factor Time series Predicting Infectious and parasitic diseases Public aspects of medicine Hong-Qiu Pan verfasserin aut Qiao Liu verfasserin aut Huan Song verfasserin aut Jian-Ming Wang verfasserin aut In Infectious Diseases of Poverty BMC, 2013 9(2020), 1, Seite 11 (DE-627)72939476X (DE-600)2689396-4 20499957 nnns volume:9 year:2020 number:1 pages:11 https://doi.org/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/article/9eef27c70e6b4ab5aaf8fb2877c9b021 kostenfrei http://link.springer.com/article/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/toc/2049-9957 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2020 1 11
allfieldsGer	10.1186/s40249-020-00771-7 doi (DE-627)DOAJ010477241 (DE-599)DOAJ9eef27c70e6b4ab5aaf8fb2877c9b021 DE-627 ger DE-627 rakwb eng RC109-216 RA1-1270 Zhong-Qi Li verfasserin aut Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings. Pulmonary tuberculosis Meteorological factor Time series Predicting Infectious and parasitic diseases Public aspects of medicine Hong-Qiu Pan verfasserin aut Qiao Liu verfasserin aut Huan Song verfasserin aut Jian-Ming Wang verfasserin aut In Infectious Diseases of Poverty BMC, 2013 9(2020), 1, Seite 11 (DE-627)72939476X (DE-600)2689396-4 20499957 nnns volume:9 year:2020 number:1 pages:11 https://doi.org/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/article/9eef27c70e6b4ab5aaf8fb2877c9b021 kostenfrei http://link.springer.com/article/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/toc/2049-9957 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2020 1 11
allfieldsSound	10.1186/s40249-020-00771-7 doi (DE-627)DOAJ010477241 (DE-599)DOAJ9eef27c70e6b4ab5aaf8fb2877c9b021 DE-627 ger DE-627 rakwb eng RC109-216 RA1-1270 Zhong-Qi Li verfasserin aut Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings. Pulmonary tuberculosis Meteorological factor Time series Predicting Infectious and parasitic diseases Public aspects of medicine Hong-Qiu Pan verfasserin aut Qiao Liu verfasserin aut Huan Song verfasserin aut Jian-Ming Wang verfasserin aut In Infectious Diseases of Poverty BMC, 2013 9(2020), 1, Seite 11 (DE-627)72939476X (DE-600)2689396-4 20499957 nnns volume:9 year:2020 number:1 pages:11 https://doi.org/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/article/9eef27c70e6b4ab5aaf8fb2877c9b021 kostenfrei http://link.springer.com/article/10.1186/s40249-020-00771-7 kostenfrei https://doaj.org/toc/2049-9957 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2020 1 11
language	English
source	In Infectious Diseases of Poverty 9(2020), 1, Seite 11 volume:9 year:2020 number:1 pages:11
sourceStr	In Infectious Diseases of Poverty 9(2020), 1, Seite 11 volume:9 year:2020 number:1 pages:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Pulmonary tuberculosis Meteorological factor Time series Predicting Infectious and parasitic diseases Public aspects of medicine
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container_title	Infectious Diseases of Poverty
authorswithroles_txt_mv	Zhong-Qi Li @@aut@@ Hong-Qiu Pan @@aut@@ Qiao Liu @@aut@@ Huan Song @@aut@@ Jian-Ming Wang @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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callnumber-first	R - Medicine
author	Zhong-Qi Li
spellingShingle	Zhong-Qi Li misc RC109-216 misc RA1-1270 misc Pulmonary tuberculosis misc Meteorological factor misc Time series misc Predicting misc Infectious and parasitic diseases misc Public aspects of medicine Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China
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topic_title	RC109-216 RA1-1270 Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China Pulmonary tuberculosis Meteorological factor Time series Predicting
topic	misc RC109-216 misc RA1-1270 misc Pulmonary tuberculosis misc Meteorological factor misc Time series misc Predicting misc Infectious and parasitic diseases misc Public aspects of medicine
topic_unstemmed	misc RC109-216 misc RA1-1270 misc Pulmonary tuberculosis misc Meteorological factor misc Time series misc Predicting misc Infectious and parasitic diseases misc Public aspects of medicine
topic_browse	misc RC109-216 misc RA1-1270 misc Pulmonary tuberculosis misc Meteorological factor misc Time series misc Predicting misc Infectious and parasitic diseases misc Public aspects of medicine
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hierarchy_parent_title	Infectious Diseases of Poverty
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title	Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China
ctrlnum	(DE-627)DOAJ010477241 (DE-599)DOAJ9eef27c70e6b4ab5aaf8fb2877c9b021
title_full	Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China
author_sort	Zhong-Qi Li
journal	Infectious Diseases of Poverty
journalStr	Infectious Diseases of Poverty
callnumber-first-code	R
lang_code	eng
isOA_bool	true
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publishDateSort	2020
contenttype_str_mv	txt
container_start_page	11
author_browse	Zhong-Qi Li Hong-Qiu Pan Qiao Liu Huan Song Jian-Ming Wang
container_volume	9
class	RC109-216 RA1-1270
format_se	Elektronische Aufsätze
author-letter	Zhong-Qi Li
doi_str_mv	10.1186/s40249-020-00771-7
author2-role	verfasserin
title_sort	comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern china
callnumber	RC109-216
title_auth	Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China
abstract	Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
abstractGer	Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
abstract_unstemmed	Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
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container_issue	1
title_short	Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China
url	https://doi.org/10.1186/s40249-020-00771-7 https://doaj.org/article/9eef27c70e6b4ab5aaf8fb2877c9b021 http://link.springer.com/article/10.1186/s40249-020-00771-7 https://doaj.org/toc/2049-9957
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author2	Hong-Qiu Pan Qiao Liu Huan Song Jian-Ming Wang
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up_date	2024-07-03T15:02:50.183Z
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