Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions.
Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions.
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DOI：
10.1017/nws.2015.22
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发表时间：
2015-09-01
期刊：
影响因子：
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通讯作者：
Sailer K
中科院分区：
文献类型：
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作者：
Potter GE;Smieszek T;Sailer K
面对面的社会接触是急性呼吸道感染的潜在重要传播途径，了解接触网络可以提高我们预测、控制和控制疫情的能力。虽然工作场所是传染病传播的重要场所，但很少有研究收集工作场所接触数据和估计工作场所接触网络。我们使用联系日记、建筑距离测量和机构结构来评估瑞士研究机构内的社会联系网络。一些联系报告不一致，表明报告错误。我们使用潜变量模型对此进行调整，联合估计真实的(未观察到的)联系人网络和特定持续时间的报告概率。我们发现，接触概率随着距离的增加而减少，研究小组成员、角色和共享项目对接触模式具有很强的预测性。仅0-5分钟接触的估计报告概率较低。对报告误差的调整改变了对持续时间分布的估计，但没有改变协变量效应的估计，对疫情预测几乎没有影响。我们的疫情模拟研究表明，基于结构和组织结构数据的网络结构的加入可以提高疫情预测模型的精度。
Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0–5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.