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Cost-effectiveness analysis on COVID-19 surveillance strategy of large-scale sports competition 被引量:2
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作者 Xuechun Wang Yiru Cai +6 位作者 Bo Zhang Xiangyu Zhang Lianhao Wang Xiangyu Yan mingchen zhao Yuan Zhang Zhongwei Jia 《Infectious Diseases of Poverty》 SCIE 2022年第2期53-62,共10页
Background:Nucleic acid test(NAT)could effectively control the spread of COVID-19 caused by large-scale sports competitions.However,quantitative analysis on the appropriate frequency of NAT is scarce,and the cost-effe... Background:Nucleic acid test(NAT)could effectively control the spread of COVID-19 caused by large-scale sports competitions.However,quantitative analysis on the appropriate frequency of NAT is scarce,and the cost-effectiveness and necessity of high-frequency NAT remain to be fully explored and validated.This study aims to optimize the COVID-19 surveillance strategies through cost-effectiveness analysis for the Tokyo 2020 Olympic Games and the upcoming Beijing 2022 Olympic Winter Games.Methods:A total of 18 scenarios were designed regarding the NAT frequency,symptom monitoring,and strengthening close-contact control.An agent-based stochastic dynamic model was used to compare the cost-effectiveness of different NAT scenarios and optimize the surveillance strategies.The dynamics of the proposed model included the arrival and departure of agents,transmission of the disease according to Poisson processes,and quarantine of agents based on regular NATs and symptom onset.Accumulative infections,cost,and incremental cost-effectiveness ratio(ICER)were simulated in the frame of the model.ICER was used to compare the cost-effectiveness of different scenarios.Univariate sensitivity analysis was performed to test the robustness of the results.Results:In Scenario 16,where the competition-related personnel(CRP)received NAT daily and national sports delegation(NSD)with quarantined infections accepted an additional NAT daily,accumulative infection was 320.90(90 initial infections),the total cost was(United States Dollar)USD 8920000,and the cost of detecting out each infection was USD 27800.Scenario 16 would reduce the total cost by USD 22570000(avoid 569.61 infections),USD 1420000(avoid 47.2 infections)compared with Scenario 10(weekly NAT,strengthened close contact control)and Scenario 7(daily NAT,no strengthened close contact control),respectively.Sensitivity analysis showed that the result was most sensitive to the change in basic reproductive number.Conclusions:High-frequency NATs such as bidaily,daily,and twice a day were cost-effective.NAT daily for CRP with strengthening close-contact control could be prioritized in defense against COVID-19 at large-scale sports competitions.This study could assist policymakers by assessing the cost-effectiveness of NAT scenarios and provide the host country with an optimal COVID-19 surveillance strategy. 展开更多
关键词 COST-EFFECTIVENESS Sports competition SURVEILLANCE COVID-19 Nucleic acid test Stochastic dynamic model
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A Landscape Analysis on Virus:based on NCBI Database
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作者 mingchen zhao Jingyuan Chen +2 位作者 Qiang Wang Zuhong Lu Zhongwei Jia 《China CDC weekly》 2022年第7期120-125,I0001-I0011,共17页
Summary What is already known about this topic?Studies indicate that viruses could spread across species,but it is difficult to know when and where such small probability events occur because it is almost impossible t... Summary What is already known about this topic?Studies indicate that viruses could spread across species,but it is difficult to know when and where such small probability events occur because it is almost impossible to design an observational study on the whole landscape.What is added by this report?We did a comprehensive analysis on the National Center for Biotechnology Information database and tried to find the time,place,and host that the viruses stayed in their long evolutionary history.What are the implications for public health practice?Public databases are helpful to understand the risk of virus infection in humans and also a cost-effective method for monitoring public health and safety events. 展开更多
关键词 DATABASE DATABASE COST
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