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; Tian-Feng He

doi:10.3390/ijerph19095385

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

Int J Environ Res Public Health. 2022 Apr 28;19(9):5385. doi: 10.3390/ijerph19095385.

Authors

Yun-Peng Chen¹, Le-Fan Liu², Yang Che³, Jing Huang⁴, Guo-Xing Li⁴, Guo-Xin Sang³, Zhi-Qiang Xuan⁵, Tian-Feng He^{3

4}

Affiliations

¹ School of Medicine, Ningbo University, 818 Fenghua Road, Ningbo 315211, China.
² Center for Health Economics, School of Economics, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China.
³ Institute of Tuberculosis Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, 237 Yongfeng Road, Ningbo 315010, China.
⁴ Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China.
⁵ Institute of Occupational Health and Radiation Protection, Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binshen Road, Hangzhou 310051, China.

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: air pollution; meteorological factor; pulmonary tuberculosis; time series.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollution*
China / epidemiology
Humans
Incidence
Meteorological Concepts
Tuberculosis, Pulmonary* / epidemiology

Grants and funding

This research was funded by Medical Technology Program Foundation of Zhejiang [No. 2021KY334; No. 2022KY1189], the Project of Zhejiang Public Welfare Fund [No. LGFLGF19H260010], the Zhejiang Medical Key Discipline [No. 07-013], and the Ningbo Health Branding Subject Fund [No. PPXK2018-10].