McKee Jae, Dallas Tad
Bioinnovation Program, Tulane University, New Orleans, LA, 70118, USA.
Department of Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA.
Infect Dis Model. 2023 Dec 28;9(1):204-213. doi: 10.1016/j.idm.2023.12.008. eCollection 2024 Mar.
Understanding and mitigating epidemic spread in complex networks requires the measurement of structural network properties associated with epidemic risk. Classic measures of epidemic thresholds like the basic reproduction number () have been adapted to account for the structure of social contact networks but still may be unable to capture epidemic potential relative to more recent measures based on spectral graph properties. Here, we explore the ability of and the spectral radius of the social contact network to estimate epidemic susceptibility. To do so, we simulate epidemics on a series of constructed (small world, scale-free, and random networks) and a collection of over 700 empirical biological social contact networks. Further, we explore how other network properties are related to these two epidemic estimators ( and spectral radius) and mean infection prevalence in simulated epidemics. Overall, we find that network properties strongly influence epidemic dynamics and the subsequent utility of and spectral radius as indicators of epidemic risk.
了解和减轻复杂网络中的疫情传播需要测量与疫情风险相关的网络结构属性。经典的疫情阈值衡量指标，如基本再生数（），已被调整以考虑社会接触网络的结构，但相对于基于谱图属性的最新指标，仍可能无法捕捉疫情潜力。在这里，我们探讨了社会接触网络的 和谱半径估计疫情易感性的能力。为此，我们在一系列构建的网络（小世界网络、无标度网络和随机网络）以及700多个经验性生物社会接触网络上模拟疫情。此外，我们还探讨了其他网络属性如何与这两个疫情估计指标（ 和谱半径）以及模拟疫情中的平均感染流行率相关。总体而言，我们发现网络属性强烈影响疫情动态以及 和谱半径作为疫情风险指标的后续效用。
