A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang

Yingdan Wang; Chunjie Gao; Tiantian Zhao; Haiyan Jiao; Ying Liao; Zengyun Hu; Lei Wang

doi:10.1371/journal.pone.0277314

A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang

PLoS One. 2023 Jan 17;18(1):e0277314. doi: 10.1371/journal.pone.0277314. eCollection 2023.

Authors

Yingdan Wang¹, Chunjie Gao¹, Tiantian Zhao², Haiyan Jiao¹, Ying Liao¹, Zengyun Hu³, Lei Wang⁴

Affiliations

¹ College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China.
² Department of Infection Prevention and Control, Puyang People's Hospital, Puyang, Henan, China.
³ State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, China.
⁴ Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, Xinjiang, China.

Abstract

In this paper, we separately constructed ARIMA, ARIMAX, and RNN models to determine whether there exists an impact of the air pollutants (such as PM2.5, PM10, CO, O3, NO2, and SO2) on the number of pulmonary tuberculosis cases from January 2014 to December 2018 in Urumqi, Xinjiang. In addition, by using a new comprehensive evaluation index DISO to compare the performance of three models, it was demonstrated that ARIMAX (1,1,2) × (0,1,1)12 + PM2.5 (lag = 12) model was the optimal one, which was applied to predict the number of pulmonary tuberculosis cases in Urumqi from January 2019 to December 2019. The predicting results were in good agreement with the actual pulmonary tuberculosis cases and shown that pulmonary tuberculosis cases obviously declined, which indicated that the policies of environmental protection and universal health checkups in Urumqi have been very effective in recent years.

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication types

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

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
China / epidemiology
Humans
Particulate Matter / analysis
Time
Tuberculosis, Pulmonary* / epidemiology

Substances

Air Pollutants
Particulate Matter

Grants and funding

Yingdan Wang and Lei Wang were supported by the Natural Science Foundation of Xinjiang (Grant No.2019D01C20),the National Natural Science Foundation of China (Grant Nos. 12061079). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.