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

ABSTRACT:

Background

Tuberculosis (TB) remains a significant public health challenge in Henan, China, requiring accurate forecasting to guide prevention and control efforts. While traditional models like autoregressive integrated moving average (ARIMA) are commonly used, they may not fully capture long-term dependencies in the data. This study evaluates the autoregressive fractionally integrated moving average (ARFIMA) model, which incorporates fractional differencing, to improve TB forecasting by better modelling long-range dependencies and seasonal patterns.

Methods

Monthly TB incidence data from January 2007 to May 2023 in Henan were collected. The data set was split into a training set (January 2007-May 2022) and a test set (June 2022-May 2023). Both ARIMA and ARFIMA models were d

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12580775 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature