Abstract
唐林,孙坤,凌倩,李东民.贝叶斯统计在艾滋病疫情估计中的应用[J].Chinese journal of Epidemiology,2020,41(3):436-441
贝叶斯统计在艾滋病疫情估计中的应用
Application of Bayesian statistics in AIDS epidemic estimation
Received:June 04, 2019  
DOI:10.3760/cma.j.issn.0254-6450.2020.03.029
KeyWord: 贝叶斯方法  艾滋病  疫情  估计
English Key Word: Bayesian methods  AIDS  Epidemic  Estimation
FundProject:国家科技重大专项(2017ZX10201101-002-005)
Author NameAffiliationE-mail
Tang Lin Department of Epidemiology, National Center for AIDS/STD Prevention and Control, Chinese Center for Disease Control and Prevention, Beijing 102206, China  
Sun Kun Department of Epidemiology, National Center for AIDS/STD Prevention and Control, Chinese Center for Disease Control and Prevention, Beijing 102206, China  
Ling Qian Department of Epidemiology, National Center for AIDS/STD Prevention and Control, Chinese Center for Disease Control and Prevention, Beijing 102206, China  
Li Dongmin Department of Epidemiology, National Center for AIDS/STD Prevention and Control, Chinese Center for Disease Control and Prevention, Beijing 102206, China lidongmin@chinaaids.cn 
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Abstract:
      随着艾滋病在世界各国的流行,波及的地域越来越广,人群更多,流行模式更为复杂,对艾滋病疫情估计和预测的方法也需要不断改进与完善。已有的疫情估计中的数理统计模型和计算机软件预测方法各有其优缺点,不同的疫情估计方法相互结合、相互印证有利于艾滋病疫情的综合评估。本文对贝叶斯统计在艾滋病疫情估计中的思想、发展、应用以及注意事项展开综述,为贝叶斯统计在艾滋病疫情估计中的进一步应用提供参考。
English Abstract:
      With the prevalence of AIDS in countries around the world, the epidemic has affected more areas and more populations, and the epidemic pattern is more complicated, so the methods for estimating and predicting AIDS epidemic need to be improved continuously. The existing mathematical statistics models and computer software prediction methods of AIDS epidemic have their own advantages and disadvantages, combination and mutual corroboration of different epidemiological estimation methods can facilitate the comprehensive assessment of the AIDS epidemic. This paper summarizes the thinking, development, application and precautions of Bayesian statistics in AIDS epidemic estimation to provide reference for the further application of Bayesian statistics in AIDS epidemic estimation.
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