Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China

Key Points

• The study aims to enhance epidemic forecasting of pulmonary tuberculosis (PTB) using machine learning techniques.
• Utilized machine learning model XGBoost for data analysis.
• Compared forecasting accuracy with traditional methods like ARIMA and Prophet.
• Focused on monthly epidemic predictions.
• XGBoost demonstrated superior extrapolation accuracy compared to ARIMA and Prophet.
• Provided reliable early-warning indicators for PTB outbreaks.
• Enabled better resource allocation and control measures in coastal urban settings.