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/K010972/2
• 负责人: Michael Tildesley
• 金额: $ 45.65万
• 依托单位: UNIVERSITY OF EXETER
• 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%2F2
• 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.

在传染病爆发的情况下，控制感染进一步传播的管理策略通常是根据以前流行病期间采用的策略或预先设想的成功可能性来实施的。然而，在疾病暴发初期，关于疾病的流行病学特性存在很大的不确定性，这可能对所选择的管理战略遏制或控制流行病的能力产生重大影响。可以建立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。然而，这些模型的有效性可能会受到限制，我们有限的知识的流行病，因为它unfolds.Extensive分析2001年口蹄疫（FMD）爆发在英国提供了宝贵的信息，疾病传播的动力学和管理行动的实施。然而，这些观察结果既针对英国环境，也针对菌株。未来在英国爆发或在美国等其他国家爆发的疫情不一定会遵循相同的模式。因此，疾病传播的关键方面和最佳应对措施在爆发之前无法解决。适应性管理（AM）试图通过将监测，评估和应对纳入管理行动来解决这一限制，以便根据对疫情动态的更好理解来修改和更新管理策略。AM框架以前曾应用于保护管理，但尚未应用于传染病的管理。AM提供了一个框架，用于从优化平均结果的早期策略（当不确定性尚未解决时）切换到优化特定模型（或模型）的结果，最佳匹配的爆发。此外，主动自适应管理寻求尽快进行这种转换，通过最初使用次优控制，允许尽快识别特定模型。因此，早期的管理措施可以用来提高知识的动态和更迅速地过渡到战略，最大限度地提高全球objective.Although我们感兴趣的是在一般应用AM的范围内爆发的情况下，在这个项目中，我们将使用2001年的口蹄疫疫情作为一个详细的，定义明确的例子。尽管经过十年的建模努力，关于最优控制的关键不确定性仍然存在，AM将使我们能够解决这些问题。我们特别建议：1.使用2001年暴发的观察监测来确定最佳适应策略以及该策略相对于静态（固定）策略的经济效益。模拟使用主动AM来区分竞争模型并选择最佳策略。为此，我们将考虑应用管理战略，以促进学习和快速更新控制政策。使用AM来确定其他疾病场景的最佳管理策略，有助于产生更一般的理解。4.利用1和2中开发的口蹄疫案例研究，我们将支持研讨会，让美国和英国政策界的成员参与对疫情的适应性管理。5.根据在工作坊中获得的理解，我们将开发一个以美国为基地的爆发个案研究，作为资助期后半期培训工作坊的主题。这个案例研究将展示AM在极端不确定性情景中的实用性。这个项目的产出将阐明AM在未来未知的传染病爆发事件中提供有效政策建议的能力。一项能够适应所观察到的疫情的单一、灵活的政策将对减少未来疫情的影响产生巨大影响。