A bayesian approach for modeling cattle movements in the United States: scaling up a partially observed network.
A bayesian approach for modeling cattle movements in the United States: scaling up a partially observed network.
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DOI：
10.1371/journal.pone.0053432
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Wennergren U
中科院分区：
文献类型：
--
作者：
Lindström T;Grear DA;Buhnerkempe M;Webb CT;Miller RS;Portacci K;Wennergren U
网络很少被完全观察到，预测未观察到的边缘是一个重要的问题，特别是在疾病传播建模中，网络用于表示接触模式。我们专注于在美国部分观察到的牛运动网络，并提出了一种方法，用于扩展到一个完整的网络的基础上贝叶斯推理，在美国的目的是通知流行病传播模型。所观察到的网络是州际兽医检查证书的10%州分层样本，州际移动所需的;描述了来自47个相邻州的大约20，000次移动，起点和目的地在县级汇总。我们解决了如何扩大10%的样本，并预测未观察到的州内运动的基础上观察到的运动距离。基于距离核的边缘预测并不简单，因为由于底层工业基础设施，移动的概率并不总是随着距离单调下降。因此，我们提出了一个空间上明确的模型，其中运动的概率取决于距离，每个县的房屋数量和动物的历史进口。我们的模型在节点级别（美国县）重新捕获观察到的网络的整体指标方面表现良好，包括度中心性和介数;与随机网络相比，表现更好。内核生成的运动网络还可以重新捕获观察到的全局网络指标，包括网络大小，传递性，互惠性和非传递性，优于随机网络。此外，预测的移动与在州一级（与政策相关的更广泛的地理级别）汇总时观察到的移动相似，并且集中在关键基础设施（如饲养场）常见的州周围。我们的结论是，该方法通常在预测粗糙的地理模式和网络结构方面表现良好，是一种很有前途的方法来生成完整的网络，将采样和未观察到的接触的不确定性。
Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.
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在非常不同的时间量表上的疾病动态：英国牲畜运动网络上的脚和口径。
DOI：
10.1098/rsif.2007.1129
发表时间：
2007-10-22
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Kao RR;Green DM;Johnson J;Kiss IZ
通讯作者：
Kiss IZ
DOI：
10.1073/pnas.0908366106
发表时间：
2009-12-29
影响因子：
11.1
作者：
Guimera, Roger;Sales-Pardo, Marta
通讯作者：
Sales-Pardo, Marta
影响因子：
4.8
作者：
Lindstroem, Tom;Hakansson, Nina;Wennergren, Uno
通讯作者：
Wennergren, Uno
影响因子：
2.3
作者：
Edmunds WJ;Kafatos G;Wallinga J;Mossong JR
通讯作者：
Mossong JR
影响因子：
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作者：
Craft ME;Caillaud D
通讯作者：
Caillaud D