评估校内接触网络以了解流感传播
Estimating within-school contact networks to understand influenza transmission
摘要：
许多流行病模型通过假设混合群体内的随机混合来近似社会接触行为（例如，家庭、学校和工作场所）。更现实的社会网络结构对流行病参数估计的影响是一个开放的探索领域。我们开发了一个详细的统计模型来估计的社会联系网络在一所高中使用的友谊网络数据和接触行为的调查。我们的接触网络模型包括课堂结构，与朋友的接触时间比非朋友更长，与朋友的接触更频繁，基于接触调查中的报告。我们进行了模拟研究，以探索哪些网络结构与流感传播相关。这些研究产生了两个关键发现。首先，我们发现，友谊网络结构的重要性的传输过程中，可以充分表示为二进制独立的指数随机图模型（ERGM）。这意味着个体水平的采样数据足以描述整个友谊网络。其次，我们发现，接触行为是充分代表了一个静态的，而不是动态的接触网络。
英文摘要：
Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than nonfriends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network.
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