RAPID: Value of Information and Structured Decision-Making for Management of Ebola

RAPID：信息和结构化决策对于埃博拉管理的价值

• 批准号: 1514704
• 负责人: Katriona Shea
• 金额: $ 20万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-15 至 2017-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514704&HistoricalAwards=false
• 关键词: RAPID; Value; Information; Structured; Decision

During an infectious disease outbreak, such as the 2014 outbreak of Ebola virus in West Africa, numerous mathematical models are created to predict the magnitude, breadth, and spatial extent of disease and to inform public health officials on how to best utilize and distribute resources. Depending on the models and the parameters of the data, they can predict vastly different outcomes. This poses a significant challenge for policy-makers to interpret and apply these tools. This research team will utilize the strengths of existing prediction models and will quantify the uncertainty in decision-making for each of the various models. The investigators will simulate alternative public health interventions using current published prediction models for the 2014 Ebola outbreak. This method will not only provide an immediate tool for the current outbreak, but it establishes a formal framework for comparing projections. This research addresses scientific uncertainty across numerous published Ebola virus models and will evaluate the ability of each model to achieve its stated objective. The researchers will systematically identify the areas of uncertainty in the models to target key sources of disagreement in management recommendations. This will be done using value of information theory and applying value of perfect information analysis. The research team plans to expand the scope of their work to include new Ebola models as they become published. The results will not select or produce a single ?best? model, but will rather articulate and reduce the uncertainty that impedes success in each of the models.

在传染病爆发期间，例如2014年西非埃博拉病毒爆发，创建了许多数学模型来预测疾病的规模，广度和空间范围，并告知公共卫生官员如何最好地利用和分配资源。根据模型和数据参数的不同，它们可以预测截然不同的结果。这对政策制定者解释和应用这些工具提出了重大挑战。该研究小组将利用现有预测模型的优势，并将量化每个模型决策的不确定性。研究人员将使用目前公布的2014年埃博拉疫情预测模型模拟替代公共卫生干预措施。这种方法不仅为当前的疫情提供了一个即时工具，而且为比较预测建立了一个正式框架。 这项研究解决了许多已发表的埃博拉病毒模型的科学不确定性，并将评估每个模型实现其既定目标的能力。研究人员将系统地确定模型中的不确定性领域，以针对管理建议中的关键分歧来源。这将充分利用信息论的价值和完全信息分析的应用价值。研究小组计划扩大他们的工作范围，包括新的埃博拉模型，因为他们成为出版。结果会不会选择或产生一个单一的？最好的？模型，而是将阐明和减少阻碍每个模型成功的不确定性。