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

机译：适应性管理与信息价值：通过流行病学干预学习

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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.
机译：严重的科学不确定性常常阻碍对疾病暴发的最佳干预。自适应管理（AM）在自然资源管理中长期使用，是一种结构化的决策方法，用于解决动态问题，该问题通过替代模型的实时评估来解决不确定性的价值。我们提出一种AM方法来设计和评估流行病学中的干预策略，使用实时监视来解决管理过程中的模型不确定性，并以口蹄疫（FMD）剔除和麻疹疫苗接种为案例研究。我们在竞争模型下使用替代干预策略的模拟，以信息的价值来量化模型不确定性对决策的影响，并量化自适应干预策略与静态干预策略的收益。由于传播空间范围的不确定性，在2001年英国口蹄疫疫情爆发期间做出的淘汰决定颇具争议。相对于在替代传播模型中将平均牲畜损失最小化的策略，在类似英国的土地上再次爆发之前，解决这种不确定性的预期收益将为45至6000万英镑。预计暴发期间的增材制造可从此预期收益中收回高达2010万英镑。与固定策略（还需要剔除连续场所）相比，AM还建议采用更保守的初始方法（剔除受感染的场所和危险的接触场）。为了最有效地针对麻疹疫苗接种，2010年在马拉维爆发的疫情中，增材制造可在受灾地区更好地分配资源;它的效用取决于对高风险人群和后勤能力的不确定性。当每天的疫苗接种率受到严重限制时，最佳的初始策略是进行小型，快速的运动;如果可以根据真正的易感人群来更新竞选目标，则可以减少大约10,000个案件的预期负担。正式纳入一项策略，以响应在爆发过程中获得的信息来更新未来的管理措施，可以改变最佳的初始响应并节省大量成本。 AM提供了使用多种模型来促进公共卫生决策的框架，以及为响应不断增强的科学理解而更新管理措施的客观基础。

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