【摘要】 目的 基于Prophet-LSTM混合模型探究节假日效应与防控措施对合肥市流感发展特征及发病趋势的影响，通过比较不同预测模型的性能，验证Prophet-LSTM模型在流感预测中的适用性。方法 收集2016—2024年合肥市流感发病数据，构建Prophet-LSTM特征分析与预测模型，分析节假日效应和防控措施对流感发病趋势的影响；同时建立ARIMA、GRU、TimeGPT等对比模型，在相同测试集上比较各模型的预测性能。结果 分析表明，元旦、春节、国庆等节假日期间流感发病率显著上升，而防控措施实施期间发病率呈现下降趋势。Prophet-LSTM模型的预测值与实际值高度吻合，其MAE(0.209)、MSE(0.195)和IA(0.914)均优于对比模型，展现出更高的预测精度和趋势拟合能力。结论 Prophet-LSTM模型能有效捕捉流感发病的时空特征，在纳入节假日效应和防控措施因素后表现出更好的预测性能，证明其在流感预测领域具有显著优势和应用价值。更多还原

【Abstract】 Objective To investigate the impact of holiday effects and prevention/control measures on the development characteristics and incidence trends of influenza in Hefei City using a Prophet-LSTM hybrid model, and to validate the applicability of the Prophet-LSTM model in influenza prediction by comparing the performance of different forecasting models. Methods Influenza incidence data from Hefei City(2016-2024) were collected to construct a Prophet-LSTM feature analysis and prediction model to analyze the impact of holiday effects and intervention measures on influenza incidence trends.Comparative models(ARIMA,GRU,and TimeGPT) were established and evaluated on the same test set. Results The data analysis revealed significantly increased influenza incidence during holidays(e.g.,New Year′s Day, Spring Festival, and National Day),while prevention and control measures led to declining trends.The Prophet-LSTM model demonstrated high consistency between the predicted and actual values, outperforming the comparative models with superior MAE(0.209),MSE(0.195),and IA(0.914),indicating higher prediction accuracy and trend-fitting capability. Conclusion The Prophet-LSTM model effectively captures spatiotemporal characteristics of influenza incidence, exhibits enhanced predictive performance when incorporating holiday effects and intervention measures, and demonstrates significant advantages and application value in influenza forecasting.更多还原