Incorporating disease and population structure into models of SIR disease in contact networks.
Incorporating disease and population structure into models of SIR disease in contact networks.
复制标题
DOI：
10.1371/journal.pone.0069162
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Volz EM
中科院分区：
文献类型：
--
作者：
Miller JC;Volz EM
我们认为最近推出的基于边缘的房室模型（EBCM）的网络中的易感染-感染-恢复（SIR）疾病的传播。这些模型与标准的传染病模型不同，它们关注的是群体中随机伴侣的状态，而不是随机个体。这种关注点的变化导致SIR疾病在具有异质度的随机网络中传播的简单分析模型。在本文中，我们扩展了这种方法来处理偏差的疾病或人口从简单的假设早期的工作。我们允许人口由于人口特征或多种类型的风险行为等影响而具有结构。我们允许疾病有更复杂的自然史。虽然我们在静态网络环境中引入了这些修改，但将它们纳入动态网络模型是很简单的。我们还考虑血清分选，这需要使用动态网络模型。我们用来导出这些推广的基本方法是广泛适用的，因此可以直接引入许多这里没有考虑的其他推广。我们的目标是双重的：提供了一些例子，概括了各种不同的人口或疾病结构的EBCM方法，并提供洞察力，如何得出这样一个模型下的新的假设。
We consider the recently introduced edge-based compartmental models (EBCM) for the spread of susceptible-infected-recovered (SIR) diseases in networks. These models differ from standard infectious disease models by focusing on the status of a random partner in the population, rather than a random individual. This change in focus leads to simple analytic models for the spread of SIR diseases in random networks with heterogeneous degree. In this paper we extend this approach to handle deviations of the disease or population from the simplistic assumptions of earlier work. We allow the population to have structure due to effects such as demographic features or multiple types of risk behavior. We allow the disease to have more complicated natural history. Although we introduce these modifications in the static network context, it is straightforward to incorporate them into dynamic network models. We also consider serosorting, which requires using dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here. Our goal is twofold: to provide a number of examples generalizing the EBCM method for various different population or disease structures and to provide insight into how to derive such a model under new sets of assumptions.
登录
查看更多内容
影响因子：
2.9
作者：
Eames, Ken T. D.;Keeling, Matt J.
通讯作者：
Keeling, Matt J.
影响因子：
1.9
作者：
Lindquist, Jennifer;Ma, Junling;Willeboordse, Frederick H.
通讯作者：
Willeboordse, Frederick H.
影响因子：
4.3
作者：
Altmann, M
通讯作者：
Altmann, M
DOI：
10.1098/rstb.1988.0108
发表时间：
1988-10-31
影响因子：
6.3
作者：
MAY, RM;ANDERSON, RM
通讯作者：
ANDERSON, RM
影响因子：
3.5
作者：
House, Thomas;Davies, Geoffrey;Keeling, Matt J.
通讯作者：
Keeling, Matt J.