Comprehensively Profiling Social Mixing Patterns in Workplace Settings to Model Pandemic Influenza Transmission

项目摘要/摘要 传染病的动态传播模型对 制定干预措施并为政策提供信息。传染病的传播性和 因此，控制策略的有效性受到社会互动的强烈影响。 因此，关于社会接触率和交配模式的准确数据是基本的 感染力计算中的参数(即易感率 被感染的个人)。尽管社交模式在社会融合中扮演着重要角色 数学模型的精确参数化，这些数据仍然有限，特别是 在工作场所环境中。关于这些人之间的社会互动的数据也有限 世卫组织远程办公或不同职业和业务模式的多样性 扇区。 我们提出了第一项多点研究，总体目标是使用标准化方法来 从美国的工作场所环境中收集社交联系数据。数据将是 从美国四家大公司严格收集。我们将使用 标准化的社交日记，以描述社交模式和 跨工作场所环境混合使用(即，当个人在办公室内工作时 工作和个人远程工作时)。我们还将全面介绍 通过收集和分析高分辨率，在公司内部建立社交联系 使用可穿戴式接近感测设备收集的测量数据。使用这些数据，我们 将开发联系矩阵和聚合联系网络，以告知代理- 基于大流行性流感传播的模型。基于代理的模型将评估 不同工作场所社交距离策略在减少或减缓工作场所社交距离方面的有效性 大流行性流感的传播。 此外，通过这个项目，我们将创建一个社交混合数据数据库，来自 工作场所设置。我们将使该数据库以及传输模型成为空间 模拟代码，使用开放获取数据中的当代标准公开提供 共享和文档。这些数据可以被传染病建模师和 生物医学和社会科学领域的其他研究人员。

PROJECT SUMMARY/ABSTRACT Dynamic transmission models of infectious diseases are increasingly influential for developing interventions and informing policy. Infectious disease transmissibility and hence, the effectiveness of control strategies, is strongly influenced by social interactions. Consequently, accurate data on social contact rates and mixing patterns are fundamental parameters in the calculation of the force of infection (i.e. the rate of susceptible individuals becoming infected). Despite the strong role social mixing patterns play in the accurate parameterization of mathematical models, these data remain limited, particularly in workplace settings. There are also limited data on the social interactions among those who telework or the diversity in patterns among different occupations and business sectors. We propose the first multi-site study with the overall goal to use standardized methods to collect social contact data from workplace settings in the United States. Data will be rigorously collected from four large companies in the United States. We will use standardized social contact diaries to characterize the patterns of social contacts and mixing across workplace environments (i.e., when an individual is performing in-office work and when an individual is teleworking). We will also comprehensively profile the social contacts within a company by collecting and analyzing high resolution measurements collected using wearable proximity-sensing devices. Using these data, we will develop contact matrices and aggregate contact networks that will inform an agent- based model of pandemic influenza transmission. The agent-based model will assess the effectiveness of various workplace social distancing strategies in reducing or slowing the transmission of pandemic influenza. Moreover, through this project, we will create a database of social mixing data from workplace settings. We will make this database, as well as the transmission model spatial simulation code, publicly available using contemporary standards in Open Access data sharing and documentation. These data can be used by infectious disease modelers and other researchers in the biomedical and social science communities.