Tail of the distribution of fatalities in epidemics

Abstract

The size that an epidemic can reach, measured in terms of the number of fatalities, is an extremely relevant quantity. It has been recently claimed [Cirillo & Taleb, Nature Physics 2020] that the size distribution of major epidemics in human history is "extremely fat-tailed", i.e., asymptotically a power law, which has important consequences for risk management. Reanalyzing this data, we find that, although the fatality distribution 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. As an example, simulation of a log-normally distributed random variable provides synthetic data whose statistics are undistinguishable from the statistics of the empirical data. Theoretical reasons justifying a power-law tail as well as limitations in the current data are also discussed.

Submitted. arXiv admin note: text overlap with arXiv:2007.06876

References in corpus (7)

• Power-law distributions in empirical data
• Tail Risk of Contagious Diseases
• Parameter estimation for power-law distributions by maximum likelihood methods
• Analysis of power-law exponents by maximum-likelihood maps
• Criticality and self-organization in branching processes: application to natural hazards
• Finite-size scaling versus dual random variables and shadow moments in the size distribution of epidemics
• Scientific comment on "Tail risk of contagious diseases"

Cited by in corpus (3)

• Lognormals, Power Laws and Double Power Laws in the Distribution of Frequencies of Harmonic Codewords from Classical Music
• Non-parametric power-law surrogates
• Finite-time scaling for epidemic processes with power-law superspreading events