Sexually Transmitted Infection Epidemics: Bayesian Prevalence and Size Estimates

性传播感染流行病：贝叶斯患病率和规模估计

• 批准号: 7628609
• 负责人: ADRIAN E RAFTERY
• 金额: $ 32.1万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7628609
• 关键词: Accounting; Acquired Immunodeficiency Syndrome; Africa; Age; Area; Benefits and Risks; Classification; Clinic; Computer software; Country; Data; Data Sources; Demographic and Health Surveys; Development; Disease; Disease model; Epidemic; Fertility; Future; Goals; Human; Individual; Link; Measurement; Methodology; Methods; Modeling; National Research Council; Output; Policies; Policy Maker; Policy Making; Population; Population Projection; Prevalence; Principal Investigator; Probability; Research; Research Personnel; Scientist; Specific qualifier value; Uncertainty; United Nations; Whales; Work; base; behavior change; interest; migration; mortality; neglect; programs

DESCRIPTION (provided by applicant): The goal of our proposal is to develop a statistical framework for probabilistic population projections and for assessing uncertainty in linked demographic-disease models. This will yield probability-based prediction intervals around all outputs of the models, allowing policy makers to assess and compare the risks and benefits of different decisions. Our framework will be Bayesian melding, which was first developed for whale population estimation and projection by the principal investigator, and applied successfully to policy-making in that context. The extensions that we propose to human populations involve overcoming technical challenges not previously overcome in the work on whales. The most common approach to communicating uncertainty in population projections is the scenario, or High-Medium-Low, approach, which has been convincingly criticized as having no probabilistic basis and leading to inconsistencies. We propose Bayesian melding as an alternative that can take account of all the available evidence and uncertainties about inputs and outputs from population projection models, to yield a predictive distribution of any quantity of policy interest. Uncertainty is even more important for linked demographic-disease models, when the goal is to forecast future population and disease prevalence in the presence of an epidemic. The United Nations Population Division does not currently issue probabilistic population projections, and has decided to assess Bayesian melding as a method for doing so, with a plan for possible implementation in the 2011 Revision of the official UN population projections. The UNAIDS Reference Group on Estimates, Models and Projections has decided to use Bayesian melding as the basis for assessing uncertainty in their demographic and prevalence projections. The development of Bayesian melding methods for these two cases raise new methodological issues that will guide our research. The specific aims of the research will be: (1) Methodological development of Bayesian melding to assess probabilistic forecasts, to deal with measurement and systematic errors, to provide a framework for model improvement, model selection and model uncertainty, and to develop more computationally efficient methods. (2) Develop Bayesian melding methods for probabilistic population projections/including fertility, mortality and migration. (3) Develop Bayesian melding methods for linked demographic-disease models, including the incorporation of multiple data sources, and the assessment of behavior change. (4) Produce and distribute software implementing the new methods produced by our research.

描述（由申请人提供）：我们的提案的目标是开发一个统计框架，用于概率人口预测和评估相关人口统计学-疾病模型的不确定性。这将围绕模型的所有输出产生基于概率的预测区间，使决策者能够评估和比较不同决策的风险和收益。我们的框架将是贝叶斯融合，这是第一次开发的鲸鱼种群估计和预测的主要研究者，并成功地应用于决策在这种情况下。我们向人类提出的扩展涉及克服以前在鲸鱼工作中没有克服的技术挑战。传达人口预测不确定性的最常见方法是假设方法，或称“高-中-低”方法，这种方法因没有概率基础并导致不一致而受到令人信服的批评。我们提出贝叶斯融合作为一种替代方案，可以考虑到所有可用的证据和不确定性的输入和输出的人口预测模型，产生一个预测分布的任何数量的政策利益。不确定性对于人口-疾病关联模型更为重要，因为其目标是在出现流行病的情况下预测未来的人口和疾病流行率。联合国人口司目前没有发布概率人口预测，并决定评估贝叶斯融合作为这样做的一种方法，并计划在2011年修订的联合国官方人口预测中可能实施。艾滋病规划署估计数、模式和预测咨商小组决定使用贝叶斯融合法作为评估其人口和流行率预测不确定性的基础。这两种情况下贝叶斯融合方法的发展提出了新的方法论问题，将指导我们的研究。研究的具体目标是：（1）贝叶斯融合评估概率预测的方法学发展，以处理测量和系统误差，为模型改进，模型选择和模型不确定性提供一个框架，并开发计算效率更高的方法。(2)为概率性人口预测/包括生育率、死亡率和移徙制定贝叶斯融合方法。(3)为人口统计学-疾病相关模型开发贝叶斯融合方法，包括整合多个数据源和行为变化评估。(4)生产和分发软件实现我们的研究产生的新方法。