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

全面分析工作场所中的社会混合模式以模拟大流行性流感传播

• 批准号: 10451468
• 负责人: Saad B. Omer
• 金额: $ 23.14万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10451468
• 关键词: Comprehensively; Profiling; Social; Mixing; Patterns

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.

项目总结/文摘