US-UK Collab Linking models and policy: Using active adaptive management for optimal control of disease outbreaks.

美英合作链接模型和政策：使用主动适应性管理来最佳控制疾病爆发。

• 批准号: BB/K010972/1
• 负责人: Michael Tildesley
• 金额: $ 50.16万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK010972%2F1
• 关键词: US; UK; Collab; Linking; models

In the event of an outbreak of an infectious disease, management strategies to control further spread of infection are generally implemented based either upon strategies employed during previous epidemics or a pre-conceived expectation of the likelihood of success. However, at the onset of an outbreak, there is a great amount of uncertainty regarding the epidemiological properties of the disease and this may have a significant influence upon the ability of the chosen management strategy to contain or control the epidemic. Mathematical models can be developed to simulate spread of disease and evaluate the effectiveness of potential control strategies. However, the effectiveness of these models may be restricted by our limited knowledge of the epidemic as it unfolds.Extensive analyses of the 2001 Foot and Mouth (FMD) outbreak in the UK have provided valuable information about both the dynamics of disease spread and the implementation of management actions. However, those observations are specific both to the UK setting and to the strain of. A future outbreak in the UK or an outbreak in another country such as the US will not necessarily follow the same pattern. Thus, key aspects of disease spread, and the optimal response, cannot be resolved until an outbreak occurs. Adaptive management (AM) seeks to address this limitation by incorporating monitoring, evaluation, and response into management actions such that management strategies can be modified and updated in response to improved understanding of the outbreak dynamics. The AM framework has previously been applied in conservation management but is yet to be applied to the management of infectious diseases.AM provides a framework for switching from the early strategy that optimises the average outcome (when uncertainties are yet to be resolved), to the one that optimises the outcome for the specific model (or models) that best matches by the outbreak at hand. Additionally, active adaptive management seeks to make this switch as soon as possible, by initially using sub-optimal controls that allow the specific model to be identified as soon as possible. Thus, early management actions can be used to improve knowledge of the dynamics and more rapidly transition to the strategy that maximizes the global objective.Although we are interested in the general application of AM to a range of outbreak scenarios; in this project we will use the 2001 FMD epidemic as a detailed, well-defined example. Despite a decade of modelling efforts, key uncertainties concerning optimal control remain, AM will allow us to address these issues. In particular we propose to: 1. Use the observed surveillance from the 2001 outbreak to identify the optimal adaptive strategy and the economic benefit of that strategy relative to a static (fixed) strategy.2. Simulate the use of active AM to discriminate amongst competing models and selection of the optimal strategy. To that end we will consider the application of management strategies to facilitate learning and rapid updating of control policies.3. Use AM to determine optimal management strategies for other disease scenarios, helping to generate a more generic understanding.4. Using the FMD case-study developed in 1 and 2, we will support workshops that engage members of the US and UK policy community in the use of adaptive management for an outbreak. 5. Based on the understanding gained in the workshops, we will develop a US-based outbreak case-study that will be used as the subject of training workshops in the second half of the grant period. This case study would demonstrate the utility of AM in a scenario of extreme uncertainty.The outputs of this project would elucidate the ability of AM to provide efficient policy advice in the event of future unknown outbreaks of infectious disease. A single, flexible policy that is able to adapt to the observed outbreak would have massive implications in reducing the impact of future outbreaks.

如果爆发感染性疾病，通常基于先前流行病期间采用的策略或对成功可能性的预期，通常会实施控制进一步传播感染的管理策略。但是，在暴发开始时，疾病的流行病学特性存在很大的不确定性，这可能会对所选管理策略遏制或控制流行病的能力产生重大影响。可以开发数学模型来模拟疾病的传播并评估潜在控制策略的有效性。但是，这些模型的有效性可能受到我们对流行病的有限了解的限制。对英国2001英尺和口爆发（FMD）爆发的扩展分析提供了有关疾病传播动态和实施管理行动的有价值的信息。但是，这些观察结果既针对英国的环境和应变。在英国的未来爆发或美国等另一个国家的爆发不一定会遵循相同的模式。因此，疾病扩散的关键方面和最佳反应在爆发之前无法解决。自适应管理（AM）试图通过将监视，评估和响应纳入管理措施中来解决此限制，以便可以修改和更新管理策略，以响应对爆发动态的理解。 AM框架先前已应用于保护管理中，但尚未应用于传染病的管理。AM提供了一个框架，用于从早期策略进行切换，以优化平均结果（尚未解决的不确定性尚待解决时），以优化一种最佳匹配的特定结果（或模型），从而优化了一种最佳的结果（或模型）。此外，主动自适应管理试图通过最初使用允许尽快识别特定模型的亚最佳控件来尽快进行此切换。因此，早期管理措施可用于改善对动态的知识，并更快地过渡到最大化全球目标的策略。尽管我们对AM在一系列爆发场景中的一般应用感兴趣；在这个项目中，我们将使用2001 FMD流行病作为一个详细的，定义明确的例子。尽管进行了十年的建模努力，但仍然存在有关最佳控制的关键不确定性，AM将使我们能够解决这些问题。特别是我们建议：1。使用2001年爆发中观察到的监视，以确定相对于静态（固定）策略的最佳适应性策略和该策略的经济利益。2。模拟主动AM在竞争模型中区分和选择最佳策略。为此，我们将考虑采用管理策略来促进控制策略的学习和快速更新。3。使用AM来确定其他疾病情景的最佳管理策略，有助于产生更通用的理解。4。使用FMD在1和2开发的FMD案例研究，我们将支持与美国和英国政策界成员一起使用自适应管理进行爆发的研讨会。 5。基于研讨会中获得的理解，我们将开发一个基于美国的暴发案件研究，该案件将在赠款期的下半年用作培训讲习班的主题。该案例研究将在极端不确定性的情况下证明AM的实用性。该项目的输出将阐明AM在未来未知的传染病爆发中提供有效的政策建议的能力。能够适应观察到的爆发的一项灵活的政策将对减少未来爆发的影响产生巨大影响。