Exploring the Role of Meteorological Factors in Predicting Incident Pulmonary Tuberculosis: A Time-Series Study in Eastern China

2020 ◽  
Author(s):  
Zhongqi Li ◽  
Hongqiu Pan ◽  
Qiao Liu ◽  
Jianming Wang
Keyword(s):  
Time Series ◽  
Pulmonary Tuberculosis ◽  
Meteorological Factors ◽  
Eastern China ◽  
Time Series Study ◽  
Series Study

scholarly journals Exploring the role of meteorological factors in predicting incident pulmonary tuberculosis: A time-series study in eastern China

2020 ◽  
Author(s):  
Zhongqi Li ◽  
Hongqiu Pan ◽  
Qiao Liu ◽  
Huan Song ◽  
Jianming Wang
Keyword(s):  
Time Series ◽  
Pulmonary Tuberculosis ◽  
Meteorological Factors ◽  
Eastern China ◽  
Arima Model ◽  
Rank Correlation ◽  
Percentage Error ◽  
Time Series Models ◽  
Cross Correlation Analysis

Abstract BackgroundMany studies have compared the performance of time-series models in predicting pulmonary tuberculosis (PTB). Few studies regarding the role of meteorological factors in predicting PTB are available. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting pulmonary tuberculosis (PTB).MethodsWe collected the monthly number of PTB cases registered in three cities of China from 2005 to 2018, and data of six meteorological factors in the same period. We constructed three time-series models, including the autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and the recurrent neural network (RNN) model. The construction of the ARIMA model did not incorporate meteorological factors, while the construction of ARIMAX and RNN models incorporated meteorological factors. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to compare the performance of models in predicting the monthly number of PTB cases in 2018.ResultsBoth the cross-correlation analysis and spearman rank correlation test showed that PTB was related to meteorological factors in the three cities. The prediction performance of both ARIMA and RNN models was improved after incorporating the meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.536%, 11.957%, and 12.360% in Xuzhou, 15.568%, 11.155%, and 14.087% in Nantong, and 9.700%, 9.660% and 12.501% 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.ConclusionsOur study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration may increase the accuracy of time series models in predicting PTB.

The role of parental self-regulation and household chaos in parent-toddler interactions: A time-series study.

2020 ◽  
Author(s):  
Sanne B. Geeraerts ◽  
Joyce Endendijk ◽  
Kirby Deater-Deckard ◽  
Jorg Huijding ◽  
Marike H. F. Deutz ◽  
...  
Keyword(s):  
Time Series ◽  
Self Regulation ◽  
Household Chaos ◽  
Time Series Study ◽  
Series Study

Meteorological factors, air pollutants, and emergency department visits for otitis media: a time series study

2017 ◽  
Vol 61 (10) ◽  
pp. 1749-1764 ◽  
Author(s):  
Massimo Gestro ◽  
Vincenzo Condemi ◽  
Luisella Bardi ◽  
Claudio Fantino ◽  
Umberto Solimene
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Otitis Media ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Emergency Department Visits ◽  
Time Series Study ◽  
Series Study

Effects of variations in meteorological factors on daily hospital visits for asthma: A time-series study

2020 ◽  
Vol 182 ◽  
pp. 109115 ◽  
Author(s):  
Hehua Zhang ◽  
Shu Liu ◽  
Zongjiao Chen ◽  
Biao Zu ◽  
Yuhong Zhao
Keyword(s):  
Time Series ◽  
Meteorological Factors ◽  
Time Series Study ◽  
Hospital Visits ◽  
Series Study

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

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Zhong-Qi Li ◽  
Hong-Qiu Pan ◽  
Qiao Liu ◽  
Huan Song ◽  
Jian-Ming Wang
Keyword(s):  
Time Series ◽  
Pulmonary Tuberculosis ◽  
Prediction Models ◽  
Meteorological Factors ◽  
Eastern China ◽  
Arima Model ◽  
Rank Correlation ◽  
Percentage Error ◽  
Time Series Models ◽  
Cross Correlation Analysis

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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