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.
Accurate data on social mixing patterns are critical for the development of valid mathematical models that simulate disease transmission dynamics and inform health policy and investment strategies. However, there has been no standardized multi-site social mixing study conducted in workplace settings in the United States that can be used to broadly inform pandemic preparedness policy in these settings. We will collect and analyze contact data, in four large companies in the United States, in order to better parameterize infectious disease models, and thus assess the effectiveness of various workplace social distancing strategies in reducing or slowing the transmission of pandemic influenza.