Linking models and policy: Using active adaptive management for optimal control o

连接模型和策略：使用主动自适应管理来实现最优控制

• 批准号: 8451706
• 负责人: Matthew Ferrari
• 金额: $ 26万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8451706
• 关键词: Active Learning; Agriculture; Algorithms; Behavior; Case Study; Characteristics; Communicable Diseases; Communities; Complex; Computer software; Conflict (Psychology); Containment; County; Crisis Intervention; Data; Decision Making; Decision Support Model; Decision Theory; Demography; Development; Disease Outbreaks; Disease model; Educational workshop; Effectiveness; Epidemic; Epidemiology; Evaluation; Event; Farming environment; Feedback; Foot-and-Mouth Disease; Hand; Health; Human; Individual; Instruction; Intervention; Knowledge; Learning; Link; Livestock; Location; Methods; Modeling; Monitor; Natural Resources; Oral cavity; Participant; Policies; Policy Maker; Property; Public Health; Research Infrastructure; Source; Structure; System; Theoretical model; Time; Training; Uncertainty; Update; Vaccination; Work; base; coping; disorder control; farmer; flexibility; foot; improved; innovation; member; novel; predictive modeling; public health emergency; real time model; response; success; surveillance data; tool; transmission process; uptake

To control disease outbreaks, critical decisions are necessary in the face of uncertainty. Though we can use models for decision support, key uncertainties about any specific epidemic, in both agricultural and human health settings, cannot be resolved a priori. By monitoring the response of an outbreak to management interventions, one can learn about both model structure and parameter values, to inform decisions. Such evaluation and assessment of competing models is often done in retrospect, and is rarely of use to real-time policies. Rather than focusing on identification of a "best model", Adaptive Management (AM) combines real-time model fitting, based on dynamic surveillance data, with stochastic optimization to select the best management action to maximize management objectives conditional on the current support for competing models. The fundamental innovation of AM is the incorporation of active learning, whereby management actions are evaluated based on their inherent benefit to achieving the objective, as well as their contribution to resolving uncertainties that limit the selection of the best action for the outbreak at hand. Though previously applied in natural resource management, AM has not been generalized for dealing with the management of infectious disease dynamics. Here we propose a multi-year effort to develop an infrastructure for model-based structured decision-making using AM for epidemic response. To demonstrate the feedback between modeling and decision-making, we propose to develop a retrospective analysis of the 2001 UK foot and mouth disease (FMD) epidemic. Through interactions with agency stake-holders in annual workshops, we will develop specific FMD model scenarios to study the interaction of uncertainties in spatial dynamics with decision-making and FMD outbreak response in the US setting. We will develop methods and software to study the FMD case study, which we will employ more generally to investigate AM of other livestock and human outbreaks in the face of various sources of spatial and logistical uncertainties that limit management. Using theoretical models, we will study the application of real-time surveillance data to resolve key uncertainties in spatial locations, transmission networks, and competing local and global objectives for the development of adaptive strategies that can optimally respond to specific outbreak settings.

为了控制疾病暴发，面对不确定性，需要进行关键决定。虽然我们可以使用 决策支持的模型，在农业和人类的任何特定流行病的关键不确定性 健康设置无法先验解决。通过监视爆发对管理的反应 干预措施，可以了解模型结构和参数值，以便为决策提供信息。这样的 经常回顾竞争模型的评估和评估，并且很少用于实时 政策。自适应管理（AM）与其专注于识别“最佳模型”的识别 基于动态监视数据的实时模型拟合，并具有随机优化以选择最佳 管理行动以最大化管理目标，以当前的竞争支持 型号。 AM的基本创新是积极学习的结合，管理层 根据其实现目标及其贡献的固有利益来评估行动 解决不确定性，以限制选择手头爆发的最佳动作。尽管 以前应用于自然资源管理，AM尚未因处理 传染病动态的管理。在这里，我们提出了多年的努力，以开发 用于基于模型的结构化决策的基础架构，使用AM进行流行反应。展示 建模与决策之间的反馈，我们建议对 2001英国脚和口腔疾病（FMD）流行病。通过年度与代理权益持有人的互动 讲习班，我们将开发特定的FMD模型方案，以研究空间中不确定性的相互作用 在美国环境中，具有决策和FMD爆发反应的动态。我们将开发方法和 研究FMD案例研究的软件，我们将更普遍地研究其他案例研究 面对各种空间和后勤不确定性来源的牲畜和人类爆发 管理。使用理论模型，我们将研究实时监视数据的应用 空间位置，传输网络以及竞争的本地和全球目标的关键不确定性 适应性策略的开发可以最佳地响应特定的暴发环境。