Application of SARIMA and other models in the incidence prediction of other infectious diarrhea diseases in Urumqi

Abstract

Abstract
This study evaluates the effectiveness of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other predictive models in forecasting the incidence of infectious diarrhea diseases in Urumqi. Using surveillance data collected over multiple years, time series analyses were conducted to capture seasonal patterns and trends. The SARIMA model was developed and compared with alternative methods such as exponential smoothing and machine learning approaches for prediction accuracy. Results demonstrate that SARIMA provides reliable forecasts of disease incidence with strong seasonal components, while integrating other models enhances predictive performance. The study highlights the utility of these models in early warning systems, facilitating timely public health interventions to control infectious diarrhea outbreaks in Urumqi.