Date of this Version
Epidemics 15 (2016) 38–55
Background: Social networks are increasingly recognized as important points of intervention, yet relatively few intervention studies of respiratory infection transmission have utilized a network design. Here we describe the design, methods, and social network structure of a randomized intervention for isolating respiratory infection cases in a university setting over a 10-week period.
Methodology/principal findings: 590 students in six residence halls enrolled in the eX-FLU study during a chain-referral recruitment process from September 2012–January 2013. Of these, 262 joined as “seed”participants, who nominated their social contacts to join the study, of which 328 “nominees” enrolled.Participants were cluster-randomized by 117 residence halls. Participants were asked to respond to weekly surveys on health behaviors, social interactions, and influenza-like illness (ILI) symptoms. Participants were randomized to either a 3-Day dorm room isolation intervention or a control group (no isolation) upon illness onset. ILI cases reported on their isolation behavior during illness and provided throat and nasal swab specimens at onset, day-three, and day-six of illness. A subsample of individuals (N = 103) participated in a sub-study using a novel smartphone application, iEpi, which collected sensor and contextually-dependent survey data on social interactions. Within the social network, participants were significantly positively assortative by intervention group, enrollment type, residence hall,iEpi participation, age, gender, race, and alcohol use (all P < 0.002).
Conclusions/significance: We identified a feasible study design for testing the impact of isolation from social networks in a university setting. These data provide an unparalleled opportunity to address questions about isolation and infection transmission, as well as insights into social networks and behaviors among college-aged students. Several important lessons were learned over the course of this project,including feasible isolation durations, the need for extensive organizational efforts, as well as the need for specialized programmers and server space for managing survey and smartphone data.