The impact of contact tracing in clustered populations.
The impact of contact tracing in clustered populations.
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DOI：
10.1371/journal.pcbi.1000721
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发表时间：
2010-03-26
影响因子：
4.3
通讯作者：
Keeling MJ
中科院分区：
文献类型：
--
作者：
House T;Keeling MJ
追踪潜在的传染性接触者已成为许多传染病控制战略的重要组成部分，从新感染的早期病例到地方性性传播感染。在这里，我们利用数学模型来考虑性传播感染的伴侣通知的情况，然而这些模型足够简单，可以得出更一般的结论。我们发现，当接触网络结构被认为是除了接触跟踪，标准的“大规模行动”模型一般是不够的。考虑到相互接触（特别是聚类）的影响，我们开发了一种改进现有的成对网络模型，我们用它来证明，在其他条件不变的情况下，聚类提高了接触跟踪的效率为一个大的参数空间区域。然而，对于高效的接触者追踪来说，这一结果有时会逆转。我们还开发了随机模拟比较，使用简单的重新布线的方法，允许生成适当的比较器网络。通过这种方式，我们有助于对传染病的网络为基础的干预的一般理论。有多种方法可以控制传染病疫苗接种和药物，如抗生素或抗病毒药物形成药物方法的一部分，但另一种途径是阻止人们相互感染。这可以通过减少与流行病学有关的接触的一般努力来实现，也可以通过更有针对性的尝试来追踪已知病例的接触者，然后对这些接触者进行隔离或治疗。这种接触追踪的影响很可能先验地强烈依赖于将人们联系在一起的接触网络。在本文中，我们开发了新的数学和计算技术来模拟聚类的影响：给定个体的任何两个联系人在网络中也相互联系的概率，从而创建三角形。通常情况下，出于直观上可以理解的原因，聚类的存在增加了接触者追踪的有效性，但我们表明，在高效接触者追踪的制度中，有时情况正好相反。
The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease. There are multiple ways to control infectious diseases—vaccination and drugs such as antibiotics or anti-virals form part of the pharmaceutical approach, however another route is to stop people infecting each other. This can be done either through general efforts to reduce epidemiologically relevant contacts, or through a more targeted attempt to trace the contacts of known cases who can then be isolated or treated. The impact of this kind of contact tracing is a priori likely to depend strongly on the network of contacts linking people together. In this paper, we develop new mathematical and computational techniques to model the impact of clustering: the probability that any two contacts of a given individual are also linked to each other in the network, creating triangles. Often, and for intuitively understandable reasons, the presence of clustering increases the efficacy of contact tracing, however we show that in the regime of highly effective contact tracing sometimes the opposite is true.
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影响因子：
3
作者：
Bansal S;Khandelwal S;Meyers LA
通讯作者：
Meyers LA
DOI：
10.1073/pnas.0510873103
发表时间：
2006-08-15
影响因子：
11.1
作者：
Riley, Steven;Ferguson, Neil M.
通讯作者：
Ferguson, Neil M.
影响因子：
1.4
作者：
Eames, K. T. D.
通讯作者：
Eames, K. T. D.
影响因子：
56.9
作者：
Lipsitch, M;Cohen, T;Murray, M
通讯作者：
Murray, M
影响因子：
8.6
作者：
Serrano, M. Angeles;Boguna, Marian
通讯作者：
Boguna, Marian