256443 Infection Proximity: A novel infection-specific exposure for network epidemiology

Tuesday, October 30, 2012 : 12:50 PM - 1:05 PM

Eric Lofgren, MSPH , Department of Epidemiology, UNC Gillings School of Global Public Health, Washington, DC
Nina Fefferman, PhD , Center for Discrete Mathematics and Theoretical Computer Science, Rutgers University, Piscataway, NJ
The study of contact networks, sexual or social, has proved invaluable in the study of the spread and prevention of infectious diseases. Recent research has extended beyond the contacts of a single individual to study how entire social mixing patterns, and the nature of the contact networks themselves influence disease risk. Doing so requires the use of mathematical measurements of the shape of a contact network, and the position of particular individuals within it. Many of these were not originally created with epidemiologic studies in mind, and measure only the wide-scale structure of the network. This information is of only secondary interest – a far more salient question is the positioning of infected individuals within the network, and the links between them and as-yet unexposed individuals.

We present a novel social network measurement, “Infection Proximity” (IP), which characterizes an individual's risk of disease from other infected members of their social contact networks. The performance of IP is then examined in the context of a simulated observational study of disease spread over a real-world social network, with IP acting as an exposure measurement. Analyzing these simulated studies using Cox proportional hazards models, IP is shown to be a good predictor of disease risk (HR = 3.43 95% CI = 1.54, 7.66), and superior to other common measures of network shape. Further theoretical and practical implications of IP in network epidemiology are then discussed.

Learning Areas:
Biostatistics, economics
Public health or related research
Social and behavioral sciences
Systems thinking models (conceptual and theoretical models), applications related to public health

Learning Objectives:
Describe the Infection Proximity exposure measurement; Assess the suitability for network-level measures of disease exposure for their own research.

Keywords: Infectious Diseases, Network Analysis

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am an infectious disease Epidemiologist with experience in disease transmission across contact networks, and the original developer of the infection proximity exposure measurement.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

Back to: 4209.0: Influenza