Tail risk of contagious diseases
Tail risk of contagious diseases
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DOI：
10.1038/s41567-020-0921-x
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发表时间：
2020-05-25
期刊：
影响因子：
19.6
通讯作者：
Taleb, Nassim Nicholas
中科院分区：
文献类型：
--
作者：
Cirillo, Pasquale;Taleb, Nassim Nicholas
本观点认为，一种称为极值理论的方法适合理解所谓的流行病爆发的尾部风险，特别是通过证明过去2500年因流行病爆发造成的死亡分布是厚尾的，并且以极端事件为主。COVID-19大流行清醒地提醒我们流行病所带来的广泛破坏，这些现象在我们的集体记忆中发挥着生动的作用，长期以来一直被认为是人类的重要风险来源。原则上，使用日益复杂的数学和计算模型来研究流行病的传播和影响，应该使政策制定者和决策者对潜在风险有更好的态势感知。然而，大多数这些模型都忽略了传染病的尾部风险、使用点预测，并且其参数的可靠性很少受到质疑并被纳入预测中。我们认为，极值理论（EVT）为评估（和管理）流行病的真实风险提供了一个自然且凭经验正确的框架，这种方法历史上是为了处理极端值（最大值或最小值）而不是平均值扮演主角（风险的根本来源）的现象而开发的。通过分析过去 2500 年的大流行病爆发数据，我们发现相关的死亡人数分布具有强烈的厚尾特征，这表明尾部风险不幸在常见的流行病学模型中被忽视。我们使用双重分布方法，结合 EVT，从无法立即用于检查的数据中提取信息。为了检查我们结论的稳健性，我们强调我们的数据要考虑到历史报告中的不精确性。我们认为，我们的研究结果具有重大意义，包括在多大程度上可以依赖分区流行病学模型和类似方法来制定政策决策。
This Perspective argues that an approach called extreme value theory is appropriate for understanding the so-called tail risk of epidemic outbreaks, in particular by demonstrating that the distribution of fatalities due to epidemic outbreaks over the past 2500 years is fat-tailed and dominated by extreme events.The COVID-19 pandemic has been a sobering reminder of the extensive damage brought about by epidemics, phenomena that play a vivid role in our collective memory, and that have long been identified as significant sources of risk for humanity. The use of increasingly sophisticated mathematical and computational models for the spreading and the implications of epidemics should, in principle, provide policy- and decision-makers with a greater situational awareness regarding their potential risk. Yet most of those models ignore the tail risk of contagious diseases, use point forecasts, and the reliability of their parameters is rarely questioned and incorporated in the projections. We argue that a natural and empirically correct framework for assessing (and managing) the real risk of pandemics is provided by extreme value theory (EVT), an approach that has historically been developed to treat phenomena in which extremes (maxima or minima) and not averages play the role of the protagonist, being the fundamental source of risk. By analysing data for pandemic outbreaks spanning over the past 2500 years, we show that the related distribution of fatalities is strongly fat-tailed, suggesting a tail risk that is unfortunately largely ignored in common epidemiological models. We use a dual distribution method, combined with EVT, to extract information from the data that is not immediately available to inspection. To check the robustness of our conclusions, we stress our data to account for the imprecision in historical reporting. We argue that our findings have significant implications, including on the extent to which compartmental epidemiological models and similar approaches can be relied upon for making policy decisions.