Estimating within-school contact networks to understand influenza transmission
arXiv:1109.0262 · doi:10.1214/11-AOAS505
Abstract
Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than nonfriends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network.
Published in at http://dx.doi.org/10.1214/11-AOAS505 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
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Cited by in corpus (6)
- Contact patterns among high school students
- Mitigation of infectious disease at school: targeted class closure vs school closure
- An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
- Estimating within-school contact networks to understand influenza transmission
- How to estimate epidemic risk from incomplete contact diaries data?
- Dynamic communicability and epidemic spread: a case study on an empirical dynamic contact network