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: 8113522
• 负责人: ADRIAN E RAFTERY
• 金额: $ 14.56万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8113522
• 关键词: Accounting; Acquired Immunodeficiency Syndrome; Africa; Age; Area; Benefits and Risks; 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疾病模型的不确定性。这将在模型的所有输出周围产生基于概率的预测间隔，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，它最初是由首席研究员为鲸鱼种群估计和预测而开发的，并成功地应用于该背景下的决策。我们对人类种群的扩展建议涉及克服以前在鲸鱼研究中没有克服的技术挑战。在人口预测中传达不确定性的最常见方法是情景法，或高-中-低方法，这种方法被批评为没有概率基础，导致不一致，这令人信服。我们提出贝叶斯融合作为一种替代方法，它可以考虑所有可用的证据和人口预测模型输入和输出的不确定性，从而产生任何数量的政策利益的预测分布。当目标是在流行病存在的情况下预测未来的人口和疾病流行情况时，不确定性对于相关的人口-疾病模型更为重要。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一种方法，并计划在2011年联合国官方人口预测的修订版中实施。艾滋病规划署估计、模型和预测参考小组决定使用贝叶斯融合作为评估其人口和流行率预测的不确定性的基础。这两种情况下贝叶斯融合方法的发展提出了新的方法学问题，将指导我们的研究。研究的具体目标是：(1)发展贝叶斯融合的方法，以评估概率预测，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框架，并开发更有效的计算方法。(2)发展概率人口预测的贝叶斯融合方法/包括生育率，死亡率和迁移。(3)为关联的人口-疾病模型开发贝叶斯融合方法，包括合并多个数据源和评估行为变化。(4)生产和分发软件，实现我们研究产生的新方法。