结构网络特征影响社会接触网络中流行病的严重程度和预测

IF 8.8 3区 医学 Q1 Medicine Infectious Disease Modelling Pub Date : 2023-12-28 DOI:10.1016/j.idm.2023.12.008
Jae McKee , Tad Dallas
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引用次数: 0

摘要

要了解和减少复杂网络中的流行病传播,就必须测量与流行病风险相关的网络结构特性。经典的流行病阈值测量方法,如基本繁殖数(R0),已被调整以考虑社会接触网络的结构,但相对于最近基于谱图特性的测量方法,仍可能无法捕捉流行病的潜力。在此,我们探讨了 R0 和社会接触网络的频谱半径在估计流行病易感性方面的能力。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)和 700 多个经验性生物社会接触网络集合上模拟了流行病。此外,我们还探讨了其他网络属性与这两个流行病估计值(R0 和频谱半径)以及模拟流行病中的平均感染率之间的关系。总之,我们发现网络属性对流行病动态以及 R0 和光谱半径作为流行病风险指标的后续效用有很大影响。
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Structural network characteristics affect epidemic severity and prediction in social contact networks

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 (R0) 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 R0 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 (R0 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 R0 and spectral radius as indicators of epidemic risk.

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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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