中国全科医学2026，Vol.29Issue(32)：4738-4744,7.DOI:10.12114/j.issn.1007-9572.2025.0285
呼吸道传染病传播风险等级评估研究:以新型冠状病毒感染的公共场所为例
Research on the Risk Level Assessment of Respiratory Infectious Disease Transmission:a Case Study of COVID-19-infected Public Places
摘要
Abstract
Background During the process of major epidemic prevention and control,rapid assessment of the risk level of infectious disease transmission is an important basis for subsequent emergency response measures.Objective This study aims to develop a risk assessment model based on machine learning(ML)algorithms to quickly identify high-risk places for respiratory infectious disease transmission,thereby facilitating rapid and precise epidemic prevention and control.Methods Data in 201 public places involving 43 COVID-19 cases in a District of Beijing during the implementation of the"dynamic zero-COVID"policy in April 2022 were collected,including place type,area,ventilation conditions,and personnel flow.An ML algorithm-based model with the secondary attack risk value of the case activity place as the dependent variable was created to assess the risk level of the infected places.Results The results of the Random Forest(RForest),Gradient Boosting Decision Tree(GBDT)and Extreme Gradient Boosting(XGBoost)models for the occurrence of close-contact transitions in the investigated places showed that GBDT performed best with receiver operating characteristic area under the curve(AUC)of 0.782 and a sensitivity of 0.647.The initial Ct value of the first case(the lowest value),health treasure scan code record query,mask wearing status of the first case,and residence time of the first case were the most important features in the model.Stratified by the place type,areas with high traffic,such as Chinese restaurants,fast food bento shops and bars weighed importantly.Conclusion The GBDT model established in this study can provide effective assistance for risk assessment of respiratory infectious disease epidemic sites,and help policy makers prioritize and make decisions under limited resources.Given the possible similar transmission mechanisms among respiratory infectious diseases,this model is expected to provide a reference for risk assessment of other respiratory infectious diseases.关键词
呼吸道传染病/风险评估/机器学习/公共卫生Key words
Respiratory infectious diseases/Risk assessment/Machine learning/Public health分类
医药卫生引用本文复制引用
胡健,刘民,焦增涛,景文展,梁万年..呼吸道传染病传播风险等级评估研究:以新型冠状病毒感染的公共场所为例[J].中国全科医学,2026,29(32):4738-4744,7.基金项目
科技创新2030-"全球重大突发传染病智能化主动监测预警系统研发及应用"重大项目(2021ZD0114100) （2021ZD0114100）
2025年北京重大呼吸道传染病研究中心课题(BJRID2025-014) （BJRID2025-014）
