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: 8837651
• 负责人: Matthew Ferrari
• 金额: $ 26.55万
• 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 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8837651
• 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; 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; public health intervention; 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.

为了控制疾病爆发，面对不确定性，必须做出关键决策。虽然我们可以使用 决策支持模型、农业和人类任何特定流行病的关键不确定性 健康设置，无法先验地解决。通过监控管理层对疫情的反应 通过干预，人们可以了解模型结构和参数值，为决策提供信息。这样的 对竞争模型的评估和评估通常是回顾性的，很少用于实时 政策。自适应管理 (AM) 不是专注于识别“最佳模型”，而是将 实时模型拟合，基于动态监测数据，通过随机优化选择最佳模型 以当前对竞争的支持为条件，实现管理目标最大化的管理行动 模型。 AM 的根本创新是主动学习的结合，通过管理 根据行动对实现目标的固有效益及其贡献进行评估 解决限制当前疫情爆发最佳行动选择的不确定性。尽管 以前应用于自然资源管理时，AM 并没有被推广到处理 传染病动态管理。在此，我们建议通过多年的努力来开发 使用 AM 进行流行病应对的基于模型的结构化决策的基础设施。展示 建模和决策之间的反馈，我们建议对 2001年英国手足口病（FMD）流行。通过与机构利益相关者的年度互动 研讨会上，我们将开发特定的 FMD 模型场景来研究空间不确定性的相互作用 美国决策和口蹄疫疫情应对的动态。我们将开发方法并 研究 FMD 案例研究的软件，我们将更广泛地使用该软件来研究其他的 AM 面对各种空间和后勤不确定性来源，牲畜和人类爆发疫情限制了 管理。利用理论模型，我们将研究实时监控数据的应用，以解决 空间位置、传输网络以及相互竞争的本地和全球目标的关键不确定性 制定能够对特定爆发环境做出最佳反应的适应性策略。