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Frequency, Duration and Intensity of Dengue Fever Epidemic Risk in Townships in Pearl River Delta and Yunnan in China, 2013 被引量:7
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作者 CHEN qian qian MENG Yu Jie +1 位作者 LI Yue qi xiao peng 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2015年第5期388-395,共8页
Dengue fever (DF), one of the neglected tropical diseases also known as breakbone fever, is a mosquito-borne disease common in the tropics and subtropics. Dengue fever is transmitted primarily by A.aegypti, resultin... Dengue fever (DF), one of the neglected tropical diseases also known as breakbone fever, is a mosquito-borne disease common in the tropics and subtropics. Dengue fever is transmitted primarily by A.aegypti, resulting in infection with any of four distinct but closely related virus serotypes. The majority of infected people experience a self-limiting clinical course. A small proportion of cases develop into life-threatening Dengue Hemorrhagic Fever or Dengue Shock Syndrome. 展开更多
关键词 Duration and Intensity of Dengue Fever Epidemic Risk in Townships in Pearl River Delta and Yunnan in China Frequency
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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
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作者 WU Jie Wen JIAO xiao Kang +7 位作者 DU Xin Hui JIAO Zeng Tao LIANG Zuo Ru PANG Ming Fan JI Han Ran CHENG Zhi Da CAI Kang Ning qi xiao peng 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2022年第5期412-418,共7页
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 assis... 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. 展开更多
关键词 COVID-19 Intervention policy simulation Machine learning Compartment model
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