Heterogeneity in susceptibility dictates the order of epidemic models
Heterogeneity in susceptibility dictates the order of epidemic models
复制标题
DOI：
10.1016/j.jtbi.2021.110839
复制
发表时间：
2021-08-05
影响因子：
2
通讯作者：
Peterson, Andrew A.
中科院分区：
文献类型：
--
作者：
Rose, Christopher;Medford, Andrew J.;Peterson, Andrew A.
流行病学的基本模型描述了一种传染病在人群中的进展，使用分区微分方程，但通常不包括感染易感性的人群水平异质性。在这里，我们结合联合收割机的传染病的广义分析框架与流行病动力学的计算模型表明，变化强烈影响感染率，而感染过程同时雕刻的易感性分布。这些关节动力学影响感染力，反过来又受到初始变异形状的影响。我们发现，某些易感性分布（指数和伽玛）是不变的，通过爆发的过程中，自然导致幂律行为的感染力;其他分布往往是塑造这些“本征分布”通过传染过程。幂律行为从根本上改变了对长期感染率的预测，并表明在类指数阶段参数化的一阶流行病模型可能会系统地、显著地高估疫情的最终严重程度。总之，我们的研究表明，有必要检查自然人群中易感性的形状，作为改善预测模型的努力的一部分，并优先考虑利用异质性来减轻传播的干预措施。(C)2021爱思唯尔有限公司保留所有权利。
The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but typically do not incorporate population-level heterogeneity in infection susceptibility. Here we combine a generalized analytical framework of contagion with computational models of epidemic dynamics to 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. 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 are often sculpted towards these "eigen-dis tributions" 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. In summary, our study suggests the need to examine the shape of susceptibility in natural populations as part of efforts to improve prediction models and to prioritize interventions that leverage heterogeneity to mitigate against spread. (C) 2021 Elsevier Ltd. All rights reserved.