Predictive modelling of air pollution affecting human tuberculosis risk on Mainland China.

In this study, we investigated the correlation between air pollution indicators and pulmonary tuberculosis (TB) incidence and mortality rates across provincial administrative regions of China from January 2013 to December 2020 to develop predictive models using machine learning. Data on TB rates and six air pollution indicators were collected and analyzed for correlations. Regression models were built using six algorithms, among which the random forest (RF) model showed superior performance. SHapley Additive exPlanations analysis helped interpret the RF model's predictions. Seasonal and lag analyses identified a 10-month optimal lag period. Seasonal autoregressive integrated moving average models were used to predict 2020 TB incidence rates, which were validated by comparing them with actual data. The results indicated significant correlations between air pollution and TB rates, highlighting that air pollution data can predict TB incidence and mortality; therefore, air pollution data can help develop public health strategies. This study emphasized the importance of integrating environmental factors into TB control efforts using artificial intelligence.