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网络动态对性传播疾病传播的影响。

Influence of network dynamics on the spread of sexually transmitted diseases.

机构信息

Consejo Nacional de Investigaciones Científicas y Técnicas, Centro Atómico Bariloche, 8400 San Carlos de Bariloche, Argentina.

出版信息

J R Soc Interface. 2012 Jun 7;9(71):1363-72. doi: 10.1098/rsif.2011.0445. Epub 2011 Nov 23.

DOI:10.1098/rsif.2011.0445
PMID:22112655
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3350717/
Abstract

Network epidemiology often assumes that the relationships defining the social network of a population are static. The dynamics of relationships is only taken indirectly into account by assuming that the relevant information to study epidemic spread is encoded in the network obtained, by considering numbers of partners accumulated over periods of time roughly proportional to the infectious period of the disease. On the other hand, models explicitly including social dynamics are often too schematic to provide a reasonable representation of a real population, or so detailed that no general conclusions can be drawn from them. Here, we present a model of social dynamics that is general enough so its parameters can be obtained by fitting data from surveys about sexual behaviour, but that can still be studied analytically, using mean-field techniques. This allows us to obtain some general results about epidemic spreading. We show that using accumulated network data to estimate the static epidemic threshold lead to a significant underestimation of that threshold. We also show that, for a dynamic network, the relative epidemic threshold is an increasing function of the infectious period of the disease, implying that the static value is a lower bound to the real threshold. A practical example is given of how to apply the model to the study of a real population.

摘要

网络流行病学通常假设人群社会网络中定义关系的因素是静态的。通过假设研究传染病传播的相关信息编码在通过考虑大致与疾病传染期成正比的时间段内积累的伙伴数量获得的网络中,间接地考虑了关系的动态性。另一方面,明确包括社会动态的模型通常过于简化,无法对真实人群进行合理的表示,或者过于详细,以至于无法从中得出一般结论。在这里,我们提出了一种社会动态模型,该模型足够通用,其参数可以通过拟合性行为调查的数据来获得,但仍然可以使用平均场技术进行分析。这使我们能够获得有关传染病传播的一些一般结果。我们表明,使用累积网络数据来估计静态传染病阈值会导致对该阈值的显著低估。我们还表明,对于动态网络,相对传染病阈值是疾病传染期的增函数,这意味着静态值是实际阈值的下限。给出了一个如何将模型应用于研究真实人群的实际示例。