Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology.
Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology.
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DOI：
10.1098/rsif.2022.0659
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发表时间：
2023-01
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概率预测支持公共卫生规划和决策，特别是在传染病紧急情况下。将多个模型的输出汇总起来，可以对结果和相关的不确定性做出更稳健的预测。虽然可以通过回顾性业绩评价来指导汇总方法的选择，但这并不总是可能的。例如，如果预测是以关于未来将如何展开的假设为条件的（例如可能的干预），那么这些假设可能永远不会实现，从而排除了预测与观察之间的任何直接比较。在这里，我们总结了聚合概率预测的文献，通过模拟说明了传染病预测的各种方法，并提出了一个策略，选择一个聚合方法时，不能使用经验验证。我们专注于线性意见池（LOP）和文森特平均，常见的方法，使不同的假设之间的预测不确定性。我们认为，聚合方法的假设应该与关于不确定性如何在不同来源的预测之间表达的假设相一致。LOP假设预测间不确定性是有意义的，应该保留，而文森特平均假设预测间不确定性类似于抽样误差，不应该保留。我们提供了一个R包来实现。鉴于多模式传染病中心的重要性日益上升，我们的工作为汇总提供了有用的指导，并加深了对不同方法的益处和风险的理解。
Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.
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发表时间：
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