Wuhan Center for Disease Prevention and Control, Wuhan, China.
Comput Math Methods Med. 2022 Jul 12;2022:6322350. doi: 10.1155/2022/6322350. eCollection 2022.
The autoregressive integrated moving average (ARIMA) model has been widely used to predict the trend of infectious diseases. This paper is aimed at analyzing the application of the ARIMA model in the prediction of the incidence trend of influenza-like illness (ILI) in Wuhan and providing a scientific basis for the prediction and prevention of influenza.
The weekly ILI data of two influenza surveillance sentinel hospitals in Wuhan City published on the website of the National Influenza Center of China were collected, and the ARIMA model was used to model the data from 2014 to 2020, to predict and verify the ILI data in 2021.
The optimal model for the incidence trend of ILI in Wuhan was ARIMA (1, 1, 1), the residuals were in line with the white noise sequence (0.018 < Ljung-Box < 30.695, > 0.05), and the relative error between the predicted value and the actual value was small, which all proved the model was practical.
ARIMA (1, 1, 1) can effectively simulate the short-term incidence trend of ILI in Wuhan.
自回归积分移动平均（ARIMA）模型已广泛应用于传染病趋势预测。本研究旨在分析 ARIMA 模型在预测武汉市流感样病例（ILI）发病率趋势中的应用，为流感的预测和预防提供科学依据。
收集中国疾病预防控制中心流感监测网络网站上公布的武汉市 2 家流感监测哨点医院的ILI 周发病数据，采用 ARIMA 模型对 2014 年至 2020 年的数据进行拟合，并对 2021 年ILI 数据进行预测和验证。
武汉市ILI 发病率趋势的最优模型为 ARIMA（1,1,1），残差符合白噪声序列（0.018<Ljung-Box<30.695，>0.05），预测值与实际值的相对误差较小，均证明模型具有实用性。
ARIMA（1,1,1）可有效模拟武汉市ILI 的短期发病趋势。
