Analysis of AIDS Transmission Based on ARIMA Model

Abstract

In response to the ongoing challenge of infectious diseases like AIDS, infectious disease experts have turned to mathematical modeling. One such model, the ARIMA (Auto Regressive Integrated Moving Average) model, has proven effective in predicting disease spread. ARIMA relies on historical data to forecast future transmission rates, enabling proactive measures to be taken. This study utilizes the ARIMA model to predict the future trajectory of AIDS cases in Guangdong Province, China, based on historical data. Initial data analysis reveals a non-linear growth pattern in AIDS cases, emphasizing the need for a more sophisticated modeling approach. Through the application of the ARIMA model with parameter selection guided by the Bayesian Information Criterion (BIC), we achieve a robust fit to historical data. The model's predictions closely align with observed data, offering valuable insights into the potential course of the disease in the region.