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Modeling workplace contact networks: The effects of organizational structure architecture and reporting errors on epidemic predictions

机译：建立工作场所联系网络的模型：组织结构架构和报告错误对流行病预测的影响

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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.
机译：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，控制和控制流行病的能力。尽管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联系数据和估计的工作场所联系网络。我们使用联系日记，建筑距离度量和制度结构来估计瑞士研究所内的社会联系网络。一些联系人报告不一致，表明报告错误。我们使用潜在变量模型对此进行调整，共同估算真实的（不可观察的）联系网络和特定于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降低，并且研究小组的成员，角色和共享项目强烈地预测了接触方式。估计的报告概率仅在0-5分钟的联系中较低。针对报告错误进行调整可以更改持续时间分布的估计，但不会更改协变量效应的估计，并且对流行病的预测影响很小。我们的流行病模拟研究表明，将基于结构和组织结构数据的网络结构包括在内可以提高流行病预测模型的准确性。

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