Integration of Spatial and Social Network Analysis in Disease Transmission Studies.
Integration of Spatial and Social Network Analysis in Disease Transmission Studies.
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DOI：
10.1080/00045608.2012.671129
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Yunus M
中科院分区：
文献类型：
--
作者：
Emch M;Root ED;Giebultowicz S;Ali M;Perez-Heydrich C;Yunus M
本研究提出了一个案例研究如何社会网络和空间分析方法可以同时用于疾病传播建模。本文首先回顾了以往研究中采用的策略，然后提供了孟加拉国农村两种细菌性腹泻病传播的例子。我们的目标是了解疾病是如何在社会上超越当地社区环境的影响而变化的。霍乱和志贺氏菌病的发病模式进行了分析，在空间和基于亲属关系的社交网络在Matlab，孟加拉国。数据包括一个有空间参照的纵向人口数据库，其中包括1983年至2003年约20万人和实验室确认的霍乱和志贺氏菌病病例。使用完整的网络设计创建家庭之间的亲属关系矩阵，并创建距离矩阵来模拟空间关系。Moran's I统计数据用于测量社会和空间矩阵内的聚类。结合空间效应空间干扰模型的建立，同时分析空间和社会的影响，同时控制当地的环境背景。结果表明，霍乱和志贺氏菌病总是集群在空间上，只有有时在社交网络。这表明当地环境对于理解这两种疾病的传播是最重要的，然而基于亲属关系的社交网络也影响它们的传播。同时进行空间和社会网络分析可以帮助我们更好地了解疾病传播，这项研究提供了几种策略。
This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.
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影响因子：
2.5
作者：
Dow, Malcolm M.
通讯作者：
Dow, Malcolm M.
影响因子：
5.4
作者：
Emch, M
通讯作者：
Emch, M
影响因子：
158.5
作者：
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通讯作者：
Fowler, James H.
影响因子：
56.9
作者：
Colwell, RR
通讯作者：
Colwell, RR
DOI：
10.4269/ajtmh.1997.56.258
发表时间：
1997-03-01
影响因子：
3.3
作者：
Ahmed, F;Clemens, JD;Haque, E
通讯作者：
Haque, E