Modeling and Predicting Pulmonary Tuberculosis Incidence and Its Association with Air Pollution and Meteorological Factors Using an ARIMAX Model: An Ecological Study in Ningbo of China

Yun-Peng Chen, Le-Fan Liu, Yang Che, Jing Huang, Guo-Xing Li, Guo-Xin Sang, Zhi-Qiang Xuan and Tian-Feng He
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Yun-Peng Chen: School of Medicine, Ningbo University, 818 Fenghua Road, Ningbo 315211, China
Le-Fan Liu: Center for Health Economics, School of Economics, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China
Yang Che: Institute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Ningbo 315010, China
Jing Huang: Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
Guo-Xing Li: Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
Guo-Xin Sang: Institute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Ningbo 315010, China
Zhi-Qiang Xuan: Institute of Occupational Health and Radiation Protection, Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binshen Road, Hangzhou 310051, China
Tian-Feng He: Institute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Ningbo 315010, China

IJERPH, 2022, vol. 19, issue 9, 1-11

Abstract: The autoregressive integrated moving average with exogenous regressors (ARIMAX) modeling studies of pulmonary tuberculosis (PTB) are still rare. This study aims to explore whether incorporating air pollution and meteorological factors can improve the performance of a time series model in predicting PTB. We collected the monthly incidence of PTB, records of six air pollutants and six meteorological factors in Ningbo of China from January 2015 to December 2019. Then, we constructed the ARIMA, univariate ARIMAX, and multivariate ARIMAX models. The ARIMAX model incorporated ambient factors, while the ARIMA model did not. After prewhitening, the cross-correlation analysis showed that PTB incidence was related to air pollution and meteorological factors with a lag effect. Air pollution and meteorological factors also had a correlation. We found that the multivariate ARIMAX model incorporating both the ozone with 0-month lag and the atmospheric pressure with 11-month lag had the best performance for predicting the incidence of PTB in 2019, with the lowest fitted mean absolute percentage error (MAPE) of 2.9097% and test MAPE of 9.2643%. However, ARIMAX has limited improvement in prediction accuracy compared with the ARIMA model. Our study also suggests the role of protecting the environment and reducing pollutants in controlling PTB and other infectious diseases.

Keywords: pulmonary tuberculosis; air pollution; meteorological factor; time series (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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