Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation|WU Jie Wen;JIAO Xiao Kang;DU Xin Hui;JIAO Zeng Tao;LIANG Zuo Ru;PANG Ming Fan;JI Han Ran;CHENG Zhi Da;CAI Kang Ning;QI Xiao Peng|Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China - 期刊导航|首站-论文投稿智能助手|论文发表|论文智能投稿|期刊自助发表推荐|杂志社快速发表|查同导刊-域田数据官方网站

典型文献

Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation

文献摘要：

Taking the Chinese city of Xiamen as an example, simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019 (COVID-19) and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures. A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios. The comparison was conducted between simulated and real cases in Xiamen. A web interface with adjustable parameters, including choice of intervention measures, intervention weights, vaccination, and viral variants, was designed for users to run the simulation. The total case number was set as the outcome. The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set. Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model. The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200, which were 25 more days and 36 fewer cases than the real situation, respectively. Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people's livelihood.

文献关键词：

中图分类号：

[1] 医药、卫生（R） / 基础医学（R3） / 病理学（R36） / 病理过程（R364）

[2] 自动化技术、计算机技术（TP） / 计算技术、计算机技术（TP3） / 计算机的应用（TP39） / 信息处理(信息加工)（TP391）

[3] 医药、卫生（R） / 药学（R9） / 药理学（R96） / 实验药理学（R965）

作者姓名：

WU Jie Wen;JIAO Xiao Kang;DU Xin Hui;JIAO Zeng Tao;LIANG Zuo Ru;PANG Ming Fan;JI Han Ran;CHENG Zhi Da;CAI Kang Ning;QI Xiao Peng

作者机构：

Center for Global Public Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China;Yidu Cloud (Beijing) Technology Co., Ltd., Beijing 100083, China

文献出处：

生物医学与环境科学（英文版）

引用格式：

[1]WU Jie Wen;JIAO Xiao Kang;DU Xin Hui;JIAO Zeng Tao;LIANG Zuo Ru;PANG Ming Fan;JI Han Ran;CHENG Zhi Da;CAI Kang Ning;QI Xiao Peng-.Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation)[J].生物医学与环境科学（英文版）,2022(05):412-418

A类：

B类：

Assessment,Benefits,Targeted,Interventions,Pandemic,Control,China,Based,Machine,Learning,Method,Service,Policy,Simulation,Taking,Chinese,city,Xiamen,example,simulation,quantitative,analysis,were,performed,transmissions,Coronavirus,Disease,influence,combinations,assist,policymakers,preparation,targeted,response,measures,machine,learning,model,was,built,estimate,effectiveness,interventions,different,scenarios,comparison,conducted,between,simulated,real,cases,web,interface,adjustable,parameters,including,choice,weights,vaccination,viral,variants,designed,users,run,total,number,set,outcome,cumulative,without,restrictions,under,strictest,closest,situation,outbreak,verify,accuracy,reliability,generated,duration,days,before,daily,dropped,zero,final,which,more,fewer,than,respectively,could,benefit,prevention,control,while,safeguarding,public,health,mitigating,impacts,people,livelihood

AB值：

0.607716

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