论文标题
易感性的异质性决定了流行病学模型的顺序
Heterogeneity in susceptibility dictates the order of epidemiological models
论文作者
论文摘要
流行病学的基本模型描述了使用分隔的微分方程通过人群的传染病进展，但在感染敏感性中不包含种群水平的异质性。我们表明，差异强烈影响感染率，而感染过程同时雕刻了易感性分布。这些关节动力学会影响感染的力，反过来受初始变异性的影响。有趣的是，我们发现在爆发过程中，某些敏感性分布（指数和伽玛）没有变化，并且自然而然地导致了感染力的幂律行为。其他分布通常会通过传染过程倾向于这些“本特征分布”。幂律行为从根本上改变了对长期感染率的预测，并表明在指数阶段被参数化的一阶流行模型可能会系统地有明显地高估爆发的最终严重性。
The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in infection susceptibility. We show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. Intriguingly, we find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions often tend towards these "eigen-distributions" through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak.
