SEIHRS_gv模型——基于短期数据预测流行性感冒样病例疫情流行趋势  

SEIHRS_gv model--predicting the influenza-like illness epidemic trend based on short term data

作　　者：金鑫[1,2] 于静波 崔爽爽 王岩[3] 于浩[4] JIN Xin;YU Jingbo;CUI Shuangshuang;WANG Yan;YU Hao(School of Medicine,Tianjin University,Tianjin 300072,China;Health Department of Tianjin Municipal Health Commission,Tianjin 300041,China;Institute of Orthopedic Research,Tianjin Hospital,Tianjin 300202,China;Institute of Business and Quality Control,Tianjin Center for Disease Control and Prevention,Tianjin 300171,China)

机构地区：[1]天津大学医学院,天津300072 [2]天津市卫生健康委员会保健处,天津300041 [3]天津市天津医院骨科研究所,天津300202 [4]天津市疾病预防控制中心业务与质量控制所,天津300171

出　　处：《中华疾病控制杂志》2024年第9期1075-1082,共8页Chinese Journal of Disease Control & Prevention

基　　金：天津市卫生健康科技项目(TJWI2022MS046)。

摘　　要：目的利用天津市流行性感冒(简称流感)样病例(influenza-like illness,ILI)监测数据,开发ILI疫情流行趋势预测模型;量化评估疫情防控措施对ILI产生的医疗负担影响。方法选取2023年11月6日―2023年11月15日天津市ILI数据进行SEIHRS_gv模型拟合,以2023年11月15日―2024年3月31日数据进行模型验证。选择均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)及决定系数(r-square,R2)评价模型预测能力。结果SEIHRS_gv模型可预测ILI疫情流行趋势、峰值及拐点,使用10 d数据进行预测,当R2达到0.85,RMSE为949.5,提高疫情防控措施强度可减少就诊人群数量。结论SEIHRS_gv模型在本轮ILI疫情预测中仅需几天数据,就可获得高精度预测结果,可作为评估医院就诊压力、指导疫情控制措施的高效预测模型。Objective To develop a prediction model for the epidemic trend of influenza-like illness using monitoring data from Tianjin City and quantitatively evaluate the impact of epidemic prevention and control measures on the medical burden caused by influenza-like illness.Methods The data from November 6,2023 to November 15,2023 were used for fitting the SEIHRS_gv model,and the data from November 15,2023 to March 31,2024 were using for validating.Root mean square error(RMSE),mean absolute error(MAE),and coefficient of determination r-square(R2)were used to evaluate the predictive ability of the model.Results The SEIHRS_gv model could predict the trend,peak,and crucial point of influenza-like illness epidemics.Using 10 days of data for prediction,with an R2 of 0.85 and an RMSE of 949.5.Increasing the intensity of epidemic prevention and control measures could reduce the number of patients seeking medical treatment.Conclusions The SEIHRS_gv model required a few days of data for prediction in this round of influenza-like illness epidemic prediction and had high accuracy in prediction results,which could serve as an efficient predictive model to evaluate the pressure of hospital visits and guide the implementation intensity of epidemic control measures.

关 键 词：流行性感冒样病例 SEIR模型 疫情防控 

分 类 号：R181.8[医药卫生—流行病学] R183[医药卫生—公共卫生与预防医学]