Synthesizing Social Proximity Networks by Combining Subjective Surveys with Digital Traces

Synthetic social contact networks play a central role in the study of epidemics and methods to control them. In this paper we propose a new methodology that combines subjective surveys and data obtained using digital devices to synthesize detailed social networks for high schools in the United States. The two data sources are diverse and have their relative merits. The proposed methodology yields high quality dynamic social proximity networks. We evaluate our methodology by carrying out a detailed structural analysis of the resulting networks. Epidemic simulations and intervention analysis using these networks provide further insights into the role of network structure on epidemics. Our results indicate that the in-class networks have a highly clustered structure with contact duration following a heavy tail distribution. SEIR-based epidemic simulations demonstrate that we may use existing theoretic graph models to fit digital trace in-class networks, but only after critical structure metrics including degree and edge weight are tuned to the real data. For practical use, the detailed model for in-class contacts using digital trace data therefore seems to add important and valuable structure needed when developing public health policies. Our methodology is quite general and can be combined with subjective assessments such as surveys and other available information. The technique is also applicable to other micro-networks such as conferences with multiple sessions, and office campuses. It is efficient and applicable in settings where data is hard or relatively expensive to obtain.