Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology

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.疾病暴发的最优干预措施常因显著的科学不确定性而受阻。适应性管理（Adaptive Management, AM）长期应用于自然资源管理领域，是一种用于解决动态问题的结构化决策方法，其考量了通过实时评估备选模型以化解不确定性的价值。我们提出了一种适应性管理方法，用于设计并评估流行病学中的干预策略：在管理推进过程中依托实时监测来化解模型不确定性，并以口蹄疫（foot-and-mouth disease, FMD）扑杀与麻疹疫苗接种作为案例研究对象。我们通过在竞争性模型下对备选干预策略开展模拟，从信息价值的维度量化模型不确定性对决策的影响，并对比量化适应性干预策略与静态干预策略的收益差异。2001年英国口蹄疫暴发期间的扑杀决策曾因传播空间尺度的不确定性引发广泛争议。相较于在备选传播模型下平均最小化牲畜损失的策略，在类英国地貌的区域暴发新疫情前化解该不确定性的预期收益可达4500万至6000万英镑。疫情期间开展的适应性管理预计可挽回该预期收益中最高达2010万英镑的部分。相较于固定策略（额外要求扑杀毗邻养殖场），适应性管理还会推荐更为保守的初始方案——即仅扑杀受感染场所与高风险接触性农场。为实现麻疹疫苗接种的最优精准部署，我们以2010年马拉维暴发的疫情为背景，发现适应性管理可实现受影响区域内资源的更优配置；其效用取决于高危人群与后勤保障能力两方面的不确定性。当日均疫苗接种率受到严格限制时，最优初始策略是开展小规模、快节奏的接种行动；若能基于真实易感人群数据更新接种目标，预计可减少约1万例新增病例的疾病负担。正式纳入一项可根据疫情过程中获取的信息来调整后续管理行动的政策，能够改变最优初始应对方案，并实现可观的成本节约。适应性管理为依托多模型开展公共卫生决策提供了框架，也为基于更完善的科学认知更新管理行动提供了客观依据。