Potter Gail E, Handcock Mark S, Longini Ira M, Halloran M Elizabeth
Center for Statistics and Quantitative Infectious Diseases, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center.
Ann Appl Stat. 2012 Mar;6(1):1-26. doi: 10.1214/11-AOAS505.
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 non-friends 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. We then compare a targeted antiviral prophylaxis intervention strategy and a grade closure intervention strategy under random mixing and network-based mixing. We find that random mixing overestimates the effect of targeted antiviral prophylaxis on the probability of an epidemic when the probability of transmission in 10 minutes of contact is less than 0.004 and underestimates it when this transmission probability is greater than 0.004. We found the same pattern for the final size of an epidemic, with a threshold transmission probability of 0.005. We also find random mixing overestimates the effect of a grade closure intervention on the probability of an epidemic and final size for all transmission probabilities. Our findings have implications for policy recommendations based on models assuming random mixing, and can inform further development of network-based models.
许多流行病模型通过假设在混合群体（如家庭、学校和工作场所）内随机混合来近似社会接触行为。更现实的社会网络结构对流行病参数估计的影响是一个有待探索的开放领域。我们开发了一个详细的统计模型，利用友谊网络数据和接触行为调查来估计一所高中内的社会接触网络。我们的接触网络模型包括课堂结构，根据接触调查中的报告，与朋友的接触持续时间比与非朋友的更长，且与朋友的接触更频繁。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。这些研究得出了两个关键发现。首先，我们发现对传播过程重要的友谊网络结构可以由二元独立指数随机图模型（ERGM）充分表示。这意味着个体层面的抽样数据足以表征整个友谊网络。其次，我们发现接触行为可以由静态而非动态接触网络充分表示。然后，我们比较了在随机混合和基于网络的混合情况下，有针对性的抗病毒预防干预策略和年级关闭干预策略。我们发现，当10分钟接触内的传播概率小于0.004时，随机混合高估了有针对性的抗病毒预防对疫情发生概率的影响，而当这个传播概率大于0.004时则低估了这种影响。对于疫情的最终规模，我们发现了相同的模式，阈值传播概率为0.005。我们还发现，对于所有传播概率，随机混合都高估了年级关闭干预对疫情发生概率和最终规模的影响。我们的研究结果对基于随机混合假设的模型的政策建议具有启示意义，并可为基于网络的模型的进一步发展提供参考。
