摘要
目的运用Prophet模型与NeuralProphet模型(NP模型)探索北京市海淀区水痘发病趋势和特征,为水痘疫情防控工作提供科学参考。方法数据来源于北京市海淀区2009年第1周至2024年第26周水痘报告发病数。采用2009—2023年的数据作为训练集,构建Prophet模型和NP模型,并运用Optuna算法对模型参数进行优化。以2024年的26周发病数据作为测试集,采用均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)和平均绝对百分比误差(mean absolute percentage error,MAPE)对各模型拟合效果进行评估。同时,对模型中的各成分进行分析。结果北京市海淀区水痘疫情每年有2个发病高峰。水痘发病数呈现逐年下降趋势,且模型中的自回归成分自2012年起波动逐渐减小。Prophet模型的RMSE、MAE和MAPE分别为9.489、7.936和27.408%;NP模型的对应指标分别为6.102、4.848和18.190%。结论Prophet模型在水痘流行趋势的预测中具有一定的适用性,而NP模型具有更高的预测性能。模型成分分析的结果,可以为评估措施效果、合理分配资源以及制定有效的防控策略提供科学依据和数据支撑。
Objective To investigate the epidemiological trends of varicella in Haidian District,Beijing,using Prophet and NeuralProphet(NP)models,and to provide evidence-based insights for optimizing varicella control strategies.Methods The weekly varicella cases data in Haidian District from Week 1 of 2009 to Week 26 of 2024 were analyzed.The Prophet and NP models were trained on data from 2009 to 2023,with hyperparameters optimized via the Optuna algorithm.Model performance was evaluated on the 2024 test set(26 weeks)using root mean squared error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).Model components were decomposed to identify contributing factors.Results Two annual incidence peaks of varicella were observed in Haidian District.The incidence of varicella exhibited a continuous decline over the years,while the autoregressive component within the model demonstrated a progressive attenuation of fluctuations starting from 2012.The Prophet model yielded RMSE,MAE,and MAPE values of 9.489,7.936 and 27.408%,respectively,while the corresponding metrics for the NP model were 6.102,4.848 and 18.190%.Conclusions The Prophet model shows moderate applicability for predicting varicella trends,whereas the NP model improves forecasting accuracy.By analyzing the components of the model,scientific evidence and data support can be provided for evaluating the effectiveness of measures,allocating resources rationally,and formulating effective prevention and control strategies.
作者
韦懿芸
孙亚敏
刘轩卓
杜婧
WEI Yiyun;SUN Yamin;LIU Xuanzhuo;DU Jing(Information Statistics Department,Center for Disease Control and Prevention of Beijing City Haidian District,100094,China)
出处
《传染病信息》
2025年第3期268-272,共5页
Infectious Disease Information
基金
首都卫生发展科研专项(首发2024-2G-30121)。
