Forecasting tuberculosis epidemics using an autoregressive fractionally integrated moving average model: a 17-year time series analysis

Tuberculosis incidence in Henan shows a clear downward trend with distinct seasonal variation. The ARFIMA model provides more accurate TB incidence forecasts than ARIMA, particularly in capturing long-term trends and seasonality. Effective management of TB at the population level requires proper monitoring and understanding of disease patterns. Forecasting serves as a critical tool for detecting deviations from expected trends, which may signal changes in disease dynamics. Continuous use of the ARFIMA model is essential for guiding public health interventions and ensuring timely responses to emerging challenges in TB control.