关于“传染病的尾部风险”的科学评论
Scientific comment on "Tail risk of contagious diseases"
摘要：
Cirillo和Taleb [Nature Phys.16，606-613（2020）]研究了人类历史上主要流行病的死亡人数。他们使用了72种流行病的数据，从雅典瘟疫（公元前429年）到COVID-19（2019-2020年），他们声称由此产生的死亡率分布是“极端肥尾”的，即，渐近幂律这对风险有重要的影响，因为死亡率分布的平均值变得无穷大。重新分析相同的数据，我们发现，虽然数据可能与幂律尾兼容，但这些结果并不是决定性的，其他分布，而不是厚尾，可以同样很好地解释数据。对数正态分布随机变量的模拟提供了其统计数据与经验数据的统计数据不可区分的合成数据。
英文摘要：
Cirillo and Taleb [Nature Phys. 16, 606-613 (2020)] study the size of major epidemics in human history in terms of the number of fatalities. Using the figures from 72 epidemics, from the plague of Athens (429 BC) to the COVID-19 (2019-2020), they claim that the resulting fatality distribution is ``extremely fat-tailed'', i.e., asymptotically a power law. This has important consequences for risk, as the mean value of the fatality distribution becomes infinite. Reanalyzing the same data, we find that, although the data may be compatible with a power-law tail, these results are not conclusive, and other distributions, not fat-tailed, could explain the data equally well. Simulation of a log-normally distributed random variable provides synthetic data whose statistics are undistinguishable from the statistics of the empirical data.
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