The impact of air pollution on the transmission of pulmonary tuberculosis

Zuqin Ding; Yaxiao Li; Xiaomeng Wang; Huling Li; Yongli Cai; Bingxian Wang; Kai Wang; Weiming Wang; Zuqin Ding; Yaxiao Li; Xiaomeng Wang; Huling Li; Yongli Cai; Bingxian Wang; Kai Wang; Weiming Wang

doi:10.3934/mbe.2020238

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 4317-4327. doi: 10.3934/mbe.2020238

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Research article Special Issues

The impact of air pollution on the transmission of pulmonary tuberculosis

1.
School of Mathematics and Statistics, Huaiyin Normal University, Huaian 223300, China
2.
College of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China
3.
Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China
^† These authors contributed equally

Received: 12 April 2020 Accepted: 12 June 2020 Published: 19 June 2020

In this paper, we investigate the relationship between the air pollution and tuberculosis cases and its prediction in Jiangsu, China by using the time-series analysis method, and find that the seasonal ARIMA(1, 1, 0)×(0, 1, 1)₁₂ model is the preferred model for predicting the TB cases in Jiangsu, China. Furthermore, we evaluate the relationship between AQI, PM2.5, PM10 and the number of TB cases, and find that the prediction accuracy of the ARIMA model is improved by adding monthly PM2.5 with 0-month lag as an external variable, i.e., ARIMA(1, 1, 0)×(0, 1, 1)₁₂+PM2.5. The results show that ARIMAX model can be a useful tool for predicting TB cases in Jiangsu, China, and it can provide a scientific basis for the prevention and treatment of TB.
- tuberculosis,
- air pollution,
- time series analysis,
- ARIMA model,
- prediction
Citation: Zuqin Ding, Yaxiao Li, Xiaomeng Wang, Huling Li, Yongli Cai, Bingxian Wang, Kai Wang, Weiming Wang. The impact of air pollution on the transmission of pulmonary tuberculosis[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 4317-4327. doi: 10.3934/mbe.2020238

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Abstract

In this paper, we investigate the relationship between the air pollution and tuberculosis cases and its prediction in Jiangsu, China by using the time-series analysis method, and find that the seasonal ARIMA(1, 1, 0)×(0, 1, 1)₁₂ model is the preferred model for predicting the TB cases in Jiangsu, China. Furthermore, we evaluate the relationship between AQI, PM2.5, PM10 and the number of TB cases, and find that the prediction accuracy of the ARIMA model is improved by adding monthly PM2.5 with 0-month lag as an external variable, i.e., ARIMA(1, 1, 0)×(0, 1, 1)₁₂+PM2.5. The results show that ARIMAX model can be a useful tool for predicting TB cases in Jiangsu, China, and it can provide a scientific basis for the prevention and treatment of TB.

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