极端新型流行病的强度和频率

M. Marani, G. Katul, W. Pan, A. Parolari
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引用次数: 195

摘要

根据长期观察到的传染病历史,对发生强烈流行病的概率估计仍然滞后或完全缺乏。在这里,我们收集并分析了跨越四个世纪的大型流行病的全球数据集。流行病的发生率随时间变化很大,但流行病强度的概率分布呈常数形式,具有缓慢衰减的代数尾,这意味着极端流行病的概率随流行强度缓慢下降。再加上最近对与环境变化相关的动物宿主疾病发生率上升的估计,这一发现表明,观察到类似于COVID-19的大流行的可能性很高(目前在人的一生中经历它的可能性约为38%),在未来几十年可能会翻一番。对流行病强度(定义为死亡人数除以全球人口和流行持续时间)和传染病暴发出现率的观察性了解,对于检验理论和模型,以及通过量化COVID-19等极端流行病的概率,为公共卫生风险评估提供信息是必要的。尽管具有重要意义,但收集和分析涵盖各种疾病的全面全球历史记录仍然是一项未开发的任务。本文编制了从1600年至今的历史流行病的全球数据集,并使用新的统计方法进行了检查,以估计每年发生极端流行病的概率。覆盖4个量级流行病强度的历史观测值遵循具有缓慢衰减幂律尾部的共同概率分布(广义帕累托分布,渐近指数= - 0.71)。每年流行病的数量变化了九倍,并显示出系统的趋势。极端流行病的年发生概率Py变化很大:从1600年到现在,“西班牙流感”(1918年至1920年)强度的事件Py变化在0.27至1.9%之间,而其今天的平均复发时间为400 y (95% CI: 332至489 y)。概率随流行强度的缓慢衰减意味着极端流行病是相对可能的,由于观测记录短和平稳分析方法,这一特性以前未被发现。根据最近对与环境变化有关的人畜共患病水库的疾病出现增长率的估计,我们估计,在未来几十年里,每年发生极端流行病的概率可能增加三倍。
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Intensity and frequency of extreme novel epidemics
Significance Estimates of the probability of occurrence of intense epidemics based on the long-observed history of infectious diseases remain lagging or lacking altogether. Here, we assemble and analyze a global dataset of large epidemics spanning four centuries. The rate of occurrence of epidemics varies widely in time, but the probability distribution of epidemic intensity assumes a constant form with a slowly decaying algebraic tail, implying that the probability of extreme epidemics decreases slowly with epidemic intensity. Together with recent estimates of increasing rates of disease emergence from animal reservoirs associated with environmental change, this finding suggests a high probability of observing pandemics similar to COVID-19 (probability of experiencing it in one’s lifetime currently about 38%), which may double in coming decades. Observational knowledge of the epidemic intensity, defined as the number of deaths divided by global population and epidemic duration, and of the rate of emergence of infectious disease outbreaks is necessary to test theory and models and to inform public health risk assessment by quantifying the probability of extreme pandemics such as COVID-19. Despite its significance, assembling and analyzing a comprehensive global historical record spanning a variety of diseases remains an unexplored task. A global dataset of historical epidemics from 1600 to present is here compiled and examined using novel statistical methods to estimate the yearly probability of occurrence of extreme epidemics. Historical observations covering four orders of magnitude of epidemic intensity follow a common probability distribution with a slowly decaying power-law tail (generalized Pareto distribution, asymptotic exponent = −0.71). The yearly number of epidemics varies ninefold and shows systematic trends. Yearly occurrence probabilities of extreme epidemics, Py, vary widely: Py of an event with the intensity of the “Spanish influenza” (1918 to 1920) varies between 0.27 and 1.9% from 1600 to present, while its mean recurrence time today is 400 y (95% CI: 332 to 489 y). The slow decay of probability with epidemic intensity implies that extreme epidemics are relatively likely, a property previously undetected due to short observational records and stationary analysis methods. Using recent estimates of the rate of increase in disease emergence from zoonotic reservoirs associated with environmental change, we estimate that the yearly probability of occurrence of extreme epidemics can increase up to threefold in the coming decades.
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