Decision-making for foot-and-mouth disease control: Objectives matter.
Decision-making for foot-and-mouth disease control: Objectives matter.
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DOI：
10.1016/j.epidem.2015.11.002
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发表时间：
2016-06
期刊：
影响因子：
3.8
通讯作者：
Ferrari MJ
中科院分区：
文献类型：
--
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
形式化的决策分析方法可以用来构建疾病控制问题，其第一步是确定一个明确而具体的目标。我们证明，在寻找控制疾病爆发的最佳控制行动时，必须制定明确定义的管理目标。我们举例说明了一种分析，可以在爆发开始时快速应用，当时有多个利益相关者参与潜在的多个目标，并且还有多个疾病模型可以比较控制行动。我们的分析结果通过突出不同管理目标之间以及分析中使用的不同模型之间的不一致领域，为政策制定者、建模者和其他利益相关者之间的后续讨论提供了框架。我们说明了这种方法的背景下，一个假设的口蹄疫（FMD）爆发在坎布里亚郡，英国使用输出从五个严格研究模拟模型的FMD传播。我们列出了每个模型和一系列目标中控制的相对排名和相对性能。结果说明了控制行动如何改变两个基本指标用于衡量管理的成功，并在统计数据用于排名控制行动，根据上述指标。我们的分析结果通过突出不同管理目标之间以及分析中使用的不同模型之间的不一致领域，为政策制定者、建模者和其他利益相关者之间的后续讨论提供了框架。这项工作是协调疾病控制问题的广泛建模工作与结构化决策框架的第一步。
Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modelers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. The output of our analysis frames subsequent discourse between policy-makers, modelers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.