PDF(Pubmed)
Abstract：
Small populations (e.g., hospitals, schools or workplaces) are characterised by high contact heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical individual contact data provide unprecedented information to characterize such heterogeneity and are increasingly available, but are usually collected over a limited period, and can suffer from observation bias. We propose an algorithm to stochastically reconstruct realistic temporal networks from individual contact data in healthcare settings (HCS) and test this approach using real data previously collected in a long-term care facility (LTCF). Our algorithm generates full networks from recorded close-proximity interactions, using hourly inter-individual contact rates and information on individuals\' wards, the categories of staff involved in contacts, and the frequency of recurring contacts. It also provides data augmentation by reconstructing contacts for days when some individuals are present in the HCS without having contacts recorded in the empirical data. Recording bias is formalized through an observation model, to allow direct comparison between the augmented and observed networks. We validate our algorithm using data collected during the i-Bird study, and compare the empirical and reconstructed networks. The algorithm was substantially more accurate to reproduce network characteristics than random graphs. The reconstructed networks reproduced well the assortativity by ward (first-third quartiles observed: 0.54-0.64; synthetic: 0.52-0.64) and the hourly staff and patient contact patterns. Importantly, the observed temporal correlation was also well reproduced (0.39-0.50 vs 0.37-0.44), indicating that our algorithm could recreate a realistic temporal structure. The algorithm consistently recreated unobserved contacts to generate full reconstructed networks for the LTCF. To conclude, we propose an approach to generate realistic temporal contact networks and reconstruct unobserved contacts from summary statistics computed using individual-level interaction networks. This could be applied and extended to generate contact networks to other HCS using limited empirical data, to subsequently inform individual-based epidemic models.
摘要：
人口较少(例如，医院,学校或工作场所）的特征是高度接触异质性和随机性会影响病原体传播动力学。经验的个人接触数据提供了前所未有的信息来表征这种异质性，并且越来越容易获得，但通常在有限的时间内收集，并且可能遭受观察偏差。我们提出了一种算法，可以从医疗机构（HCS）中的个人联系人数据中随机重建现实的时间网络，并使用先前在长期护理机构（LTCF）中收集的真实数据来测试这种方法。我们的算法从记录的近距离交互生成完整的网络，使用每小时的个人间接触率和个人病房信息，接触人员的类别，以及反复接触的频率。它还通过在某些个人存在于HCS中而没有在经验数据中记录联系人的情况下重建几天的联系人来提供数据增强。记录偏差通过观察模型形式化，允许在增强网络和观察网络之间进行直接比较。我们使用i-Bird研究期间收集的数据验证了我们的算法，并比较经验网络和重构网络。该算法比随机图更准确地再现网络特征。重建的网络很好地再现了病房的多样性（观察到的第一至第三四分位数：0.54-0.64；合成：0.52-0.64）以及每小时的工作人员和患者接触模式。重要的是,观察到的时间相关性也得到了很好的再现(0.39-0.50vs0.37-0.44)，表明我们的算法可以重建一个真实的时间结构。该算法一致地重新创建未观察到的接触以生成用于LTCF的完整的重构网络。最后，我们提出了一种方法来生成现实的时间接触网络，并从使用个体级交互网络计算的汇总统计数据中重建未观察到的接触。这可以应用和扩展到使用有限的经验数据生成其他HCS的接触网络，随后为基于个人的流行病模型提供信息。
