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/K010972/4
• 负责人: Michael Tildesley
• 金额: $ 24.72万
• 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: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK010972%2F4
• 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.

在传染病暴发的情况下，控制感染进一步蔓延的管理战略通常是根据以前流行期间采用的战略或对成功可能性的预先预期来实施的。然而，在疫情爆发时，关于疾病的流行病学特征存在很大的不确定性，这可能对所选管理战略遏制或控制疫情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。然而，由于我们对疫情的了解有限，这些模型的有效性可能会受到限制。对2001年英国口蹄疫(FMD)疫情的深入分析提供了有关疾病传播动力学和管理行动实施的宝贵信息。然而，这些观察结果是特定于英国的环境和压力的。未来在英国爆发的疫情或在美国等其他国家爆发的疫情不一定遵循同样的模式。因此，在暴发发生之前，疾病传播的关键方面和最佳应对措施都无法解决。适应性管理(AM)试图通过将监测、评估和响应纳入管理行动来解决这一限制，以便可以根据对疫情动态的更好理解来修改和更新管理战略。AM框架以前曾应用于养护管理，但尚未应用于传染病的管理。AM提供了一个框架，用于从早期优化平均结果的策略(当不确定性尚未解决时)转换为优化特定模型(或多个模型)的结果的框架，该模型与手头的暴发最匹配。此外，主动自适应管理寻求尽快实现这一转变，首先使用次优控制，使特定模型能够尽快被识别。因此，早期的管理行动可以用来提高对动态的了解，并更快地过渡到使全球目标最大化的战略。尽管我们对AM在一系列爆发情景中的一般应用感兴趣，但在本项目中，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。具体而言，我们建议：1.利用从2001年暴发中观察到的监测结果，确定最优的适应性策略以及该策略相对于静态(固定)策略的经济效益。模拟使用有源AM来区分竞争模型和选择最优策略。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为其他疾病情景确定最佳管理策略，帮助产生更一般的理解。利用在1和2中开发的口蹄疫案例研究，我们将支持让美国和英国政策界成员参与使用适应性管理来应对疫情的研讨会。5.根据讲习班取得的谅解，我们将编写一份以美国为基础的暴发个案研究报告，作为赠款期间后半期培训讲习班的主题。这一案例研究将展示AM在极端不确定情况下的效用，该项目的输出将阐明AM在未来未知传染病爆发时提供有效政策建议的能力。能够适应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重大影响。

项目成果

Dynamics of the 2004 avian influenza H5N1 outbreak in Thailand: The role of duck farming, sequential model fitting and control.

2004年泰国鸟类流感H5N1爆发的动力学：鸭式耕作，顺序模型拟合和控制的作用。

• DOI: 10.1016/j.prevetmed.2018.09.014
• 发表时间: 2018-11-01
• 期刊: Preventive veterinary medicine
• 影响因子: 2.6
• 作者: Retkute R;Jewell CP;Van Boeckel TP;Zhang G;Xiao X;Thanapongtharm W;Keeling M;Gilbert M;Tildesley MJ
• 通讯作者: Tildesley MJ

Decision-making for foot-and-mouth disease control: Objectives matter.

• DOI: 10.1016/j.epidem.2015.11.002
• 发表时间: 2016-06
• 期刊: Epidemics
• 影响因子: 3.8
• 作者: Probert WJ;Shea K;Fonnesbeck CJ;Runge MC;Carpenter TE;Dürr S;Garner MG;Harvey N;Stevenson MA;Webb CT;Werkman M;Tildesley MJ;Ferrari MJ
• 通讯作者: Ferrari MJ

Table S2 from Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study

表 S2 来自对最佳疫情控制的流行病学和操作不确定性的同时评估：以埃博拉病毒为案例研究

• DOI: 10.6084/m9.figshare.8215898
• 发表时间: 2019
• 作者: Shou-Li LI

Real-time decision-making during emergency disease outbreaks.

• DOI: 10.1371/journal.pcbi.1006202
• 发表时间: 2018-07
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Probert WJM;Jewell CP;Werkman M;Fonnesbeck CJ;Goto Y;Runge MC;Sekiguchi S;Shea K;Keeling MJ;Ferrari MJ;Tildesley MJ
• 通讯作者: Tildesley MJ

Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns.

• DOI: 10.1371/journal.pcbi.1005318
• 发表时间: 2017-02
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Bradbury NV;Probert WJ;Shea K;Runge MC;Fonnesbeck CJ;Keeling MJ;Ferrari MJ;Tildesley MJ
• 通讯作者: Tildesley MJ