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沿连续统的 SIS(SIS(c))疾病流行病学模型和有向贸易网络上的疾病控制。

SIS along a continuum (SIS(c)) epidemiological modelling and control of diseases on directed trade networks.

机构信息

INRA, UR 341 Mathématiques et Informatique Appliquées, 78350 Jouy-en-Josas, France.

出版信息

Math Biosci. 2012 Mar;236(1):44-52. doi: 10.1016/j.mbs.2012.01.004. Epub 2012 Jan 28.

Abstract

Network theory has been applied to many aspects of biosciences, including epidemiology. Most epidemiological models in networks, however, have used the standard assumption of either susceptible or infected individuals. In some cases (e.g. the spread of Phytophthora ramorum in plant trade networks), a continuum in the infection status of nodes can better capture the reality of epidemics in networks. In this paper, a Susceptible-Infected-Susceptible model along a continuum in the infection status (SIS(c)) is presented, using as a case study directed networks and two parameters governing the epidemic process (probability of infection persistence (p(p)) and of infection transmission (p(t)). The previously empirically reported linear epidemic threshold in a plot of p(p) as a function of p(t) (Pautasso and Jeger, 2008) is derived analytically. Also the previously observed negative correlation between the epidemic threshold and the correlation between links in and out of nodes (Moslonka-Lefebvre et al., 2009) is justified analytically. A simple algorithm to calculate the threshold conditions is introduced. Additionally, a control strategy based on targeting market hierarchical categories such as producers, wholesalers and retailers is presented and applied to a realistic reconstruction of the UK horticultural trade network. Finally, various applications (e.g., seed exchange networks, food trade, spread of ideas) and potential refinements of the SIS(c) model are discussed.

摘要

网络理论已被应用于生物科学的许多领域,包括流行病学。然而,网络中的大多数流行病学模型都采用了易感或感染个体的标准假设。在某些情况下(例如,植物贸易网络中 Phytophthora ramorum 的传播),节点感染状态的连续统可以更好地捕捉网络中流行病的现实情况。在本文中,提出了一种沿感染状态连续统的易感染-易感染-易感染模型(SIS(c)),并以有向网络和两个控制流行病过程的参数(感染持续概率(p(p))和感染传播概率(p(t)))作为案例研究。先前在 p(p)作为 p(t)函数的图中报告的线性流行病阈值(Pautasso 和 Jeger,2008)是通过解析推导得出的。同样,先前观察到的节点内外连接之间的流行病阈值与相关性之间的负相关关系(Moslonka-Lefebvre 等人,2009)也是通过解析得到证明的。介绍了一种计算阈值条件的简单算法。此外,提出了一种基于针对生产者、批发商和零售商等市场层次类别的控制策略,并将其应用于英国园艺贸易网络的实际重建。最后,讨论了 SIS(c)模型的各种应用(例如,种子交换网络、食品贸易、思想传播)和潜在改进。

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