Evolutionary dynamics of incubation periods
Evolutionary dynamics of incubation periods
复制标题
潜伏期的进化动力学
作者：
Bertrand Ottino;Jacob G. Scott;S. Strogatz
疾病的潜伏期是指从开始的病理事件到疾病发作之间的时间1。对于伤寒2、3、小儿麻痹症4、麻疹5、白血病6和许多其他疾病7 -10，潜伏期是高度可变的。一些受影响的人需要比平均更长的时间才能显示出症状，导致潜伏期的分布是右偏的，通常近似于对数正态8 -10。虽然这种统计模式是在60多年前发现的，但解释它的普遍性仍然是一个悬而未决的问题。在这里，我们提出了一个基于图12 -18的进化动力学的解释。对于简单的模型的突变体或病原体入侵网络结构的健康细胞的人口，我们发现，出现了广泛的假设入侵者健身，竞争动力学和网络结构的潜伏期的偏斜分布。偏斜源于随机机制与概率论中的两个经典问题：优惠券收集器和随机行走19，20。不像以前的解释11，21，严重依赖于异质性，我们的结果甚至适用于同质人群。因此，我们预测，两个同样健康的人受到相同剂量的同样致病剂，可能只是偶然，显示出显着不同的时间进程的疾病。
The incubation period of a disease is the time between an initiating pathologic event and the onset of symptoms1. For typhoid fever2,3, polio4, measles5, leukemia6 and many other diseases7–10, the incubation period is highly variable. Some affected people take much longer than average to show symptoms, leading to a distribution of incubation periods that is right skewed and often approximately lognormal8–10. Although this statistical pattern was discovered more than sixty years ago8, it remains an open question to explain its ubiquity11. Here we propose an explanation based on evolutionary dynamics on graphs12–18. For simple models of a mutant or pathogen invading a network-structured population of healthy cells, we show that skewed distributions of incubation periods emerge for a wide range of assumptions about invader fitness, competition dynamics, and network structure. The skewness stems from stochastic mechanisms associated with two classic problems in probability theory: the coupon collector and the random walk19,20. Unlike previous explanations11,21 that rely crucially on heterogeneity, our results hold even for homogeneous populations. Thus, we predict that two equally healthy individuals subjected to equal doses of equally pathogenic agents may, by chance alone, show remarkably different time courses of disease.