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

为了控制疾病暴发，在面临不确定性的情况下，关键决定是必要的。尽管我们可以利用 决策支持模型，关于农业和人类中任何特定流行病的关键不确定性 健康设置，不能先验解决。通过监测疫情对管理层的反应 通过干预，人们可以了解模型结构和参数值，从而为决策提供参考。是这样的 对竞争模型的评估和评估通常是在回顾中进行的，很少用于实时 政策。适应性管理(AM)并不是专注于识别“最佳模型”，而是结合了 实时模型拟合，基于动态监测数据，结合随机优化选择最优 管理行动，以最大限度地实现管理目标，条件是目前对竞争对手的支持 模特们。AM的根本创新是融入了主动学习，从而使管理 根据行动对实现目标的内在益处以及他们的贡献来评估行动 解决限制选择手头疫情最佳行动的不确定因素。尽管 在以前应用于自然资源管理的AM中，并没有被推广到处理 传染病动态管理。在这里，我们提出了一项多年的努力，以开发一种 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。为了证明 在建模和决策之间的反馈，我们建议对 2001年英国口蹄疫流行。通过与机构利益相关者的互动，每年 ，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。 美国背景下的决策和口蹄疫疫情应对的动态。我们将制定方法和 研究FMD案例研究的软件，我们将更普遍地使用该软件来调查其他客户经理 在各种空间和后勤不确定因素限制下的牲畜和人类暴发 管理层。利用理论模型，我们将研究应用实时监控数据来解决 空间位置、传输网络以及相互竞争的本地和全球目标中的关键不确定性 制定能够以最佳方式应对特定暴发环境的适应性战略。

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.