Adaptive management and the value of information: learning via intervention in epidemiology.
Adaptive management and the value of information: learning via intervention in epidemiology.
复制标题
适应性管理和信息的价值：通过流行病学干预进行学习。
DOI：
10.1371/journal.pbio.1001970
复制
发表时间：
2014-10
期刊：
影响因子：
9.8
通讯作者：
Ferrari MJ
中科院分区：
文献类型：
--
作者：
Shea K;Tildesley MJ;Runge MC;Fonnesbeck CJ;Ferrari MJ
这篇研究文章探讨了将适应性管理方法应用于疾病爆发的好处，发现正式整合科学和政策可以减少不确定性并改善疾病管理结果。对疾病爆发的最佳干预往往受到严重的科学不确定性的阻碍。自适应管理 (AM) 长期用于自然资源管理，是一种解决动态问题的结构化决策方法，通过实时评估替代模型来解决不确定性的价值。我们提出了一种 AM 方法来设计和评估流行病学干预策略，利用实时监测来解决管理过程中的模型不确定性，并以口蹄疫 (FMD) 扑杀和麻疹疫苗接种作为案例研究。我们使用竞争模型下的替代干预策略的模拟来量化模型不确定性对决策的影响（就信息价值而言），并量化自适应干预策略与静态干预策略的好处。由于传播空间规模的不确定性，2001 年英国口蹄疫爆发期间的扑杀决定存在争议。相对于最大限度地减少替代传播模型平均牲畜损失的策略，在类似英国的地区出现新的疫情爆发之前解决这种不确定性的预期效益将为 45-6000 万英镑。疫情期间 AM 预计将收回高达 2010 万英镑的预期收益。 AM 还建议采用比固定策略（另外需要剔除邻近场所）更保守的初始方法（剔除受感染的场所和危险的接触农场）。为了实现麻疹疫苗接种的最佳目标，根据 2010 年马拉维爆发的麻疹疫情，AM 可以更好地在受影响地区分配资源；其效用取决于高危人群和后勤能力的不确定性。当每日疫苗接种率受到高度限制时，最佳的初始策略是开展小规模、快速的疫苗接种活动；如果能够根据真正的易感人群更新运动目标，预计可减少约 10,000 例病例的负担。正式纳入一项政策来更新未来的管理行动，以响应疫情爆发过程中获得的信息，可以改变最佳的初始反应，并节省大量成本。 AM 提供了一个使用多种模型促进公共卫生决策的框架，并为更新管理行动以响应提高的科学理解提供了客观基础。如果对疾病爆发的应对管理不善，可能会造成生命损失和不必要的金钱浪费。缺乏对疾病动态以及我们的控制策略对这些动态的影响的了解，意味着很难尽最大努力管理此类流行病学问题。在这里，我们提出了一种适应性管理方法，使研究人员能够利用在疫情爆发期间获得的知识来更新正在进行的干预措施，从而将科学发现转化为改进的政策。我们探讨了适应性管理对牲畜口蹄疫爆发和人类麻疹疫苗接种策略的影响。在这两种特殊情况下，计划更新管理措施会导致建议采用比未预期管理变化的情况更不激进的初始方法。根据 2001 年在英国观察到的动态，我们证明，在减少口蹄疫疫情中宰杀牲畜损失方面，预计可节省高达 2000 万英镑。同样，在 2010 年马拉维观察到的麻疹疫情中，可以避免多达 10,000 例病例。适应性管理可以实时改善我们的理解，从而改善管理工作，并具有潜在的显着的积极财务和健康效益。
This Research Article explores the benefits of applying Adaptive Management approaches to disease outbreaks, finding that formally integrating science and policy allows one to reduce uncertainty and improve disease management outcomes. Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding. If the response to a disease outbreak is poorly managed, lives may be lost and money wasted unnecessarily. Lack of knowledge about the disease dynamics, and about the effects of our control strategies on those dynamics, means that it is difficult to do the best job possible managing such epidemiological problems. Here, we present an adaptive management approach that allows researchers to use knowledge gained during an outbreak to update ongoing interventions, thereby translating scientific discovery into improved policy. We explore the implications of adaptive management for foot-and-mouth disease outbreaks in livestock and for measles vaccination strategies in humans. In these two particular cases, planning to update management actions leads to the recommendation of a less aggressive initial approach than if changes in management are not anticipated. We demonstrate expected savings of up to £20 million in terms of lower livestock losses to culling in a foot-and-mouth outbreak based on the dynamics observed in the UK in 2001. Similarly, up to 10,000 cases could have been averted in a measles outbreak like the one observed in Malawi in 2010. Adaptive management allows real-time improvement of our understanding, and hence of management efforts, with potentially significant positive financial and health benefits.
登录
查看更多内容
影响因子：
56.9
作者：
Lipsitch, M;Cohen, T;Murray, M
通讯作者：
Murray, M
影响因子：
56.9
作者：
Ferguson, NM;Donnelly, CA;Anderson, RM
通讯作者：
Anderson, RM
影响因子：
56.9
作者：
Keeling, MJ;Woolhouse, MEJ;Grenfell, BT
通讯作者：
Grenfell, BT
影响因子：
3.8
作者：
Cox, LA;Popken, DA;Sahu, R
通讯作者：
Sahu, R
影响因子：
64.8
作者：
Cauchemez, Simon;Valleron, Alain-Jacques;Ferguson, Neil M.
通讯作者：
Ferguson, Neil M.