Estimating within-school contact networks to understand influenza transmission

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 non-friends 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. We then compare a targeted antiviral prophylaxis intervention strategy and a grade closure intervention strategy under random mixing and network-based mixing. We find that random mixing overestimates the effect of targeted antiviral prophylaxis on the probability of an epidemic when the probability of transmission in 10 minutes of contact is less than 0.004 and underestimates it when this transmission probability is greater than 0.004. We found the same pattern for the final size of an epidemic, with a threshold transmission probability of 0.005. We also find random mixing overestimates the effect of a grade closure intervention on the probability of an epidemic and final size for all transmission probabilities. Our findings have implications for policy recommendations based on models assuming random mixing, and can inform further development of network-based models.

Factors Associated with Post-Seasonal Serological Titer and Risk Factors for Infection with the Pandemic A/H1N1 Virus in the French General Population

“…Interestingly, we did not find a significant association of GMT with daily duration of meetings with children younger than 10 years old regardless of location, suggesting that school favors transmissions by a particular pattern of contacts or environmental characteristics [67], [68].…”

Contact Patterns among High School Students

“…Many efforts have therefore been devoted to the collection of data on human contact patterns in various settings and environments in the last years [3]. The research community has moreover started to systematically take advantage of recent technological advances to move from methods ranging from diaries and surveys [4][5][6][7][8][9][10][11][12][13] to new technologies based on wearable sensors able to detect close proximity [13][14][15][16][17][18] and even face-to-face contacts of individuals [19][20][21][22][23][24][25]. Biases due to self-reporting are thus avoided [10,13] and high-resolution data can be collected in an objective way, allowing to parametrize and inform datadriven models describing human behavior [26][27][28] and epidemic spread in specific settings [21].…”

Mitigation of infectious disease at school: targeted class closure vs school closure

“…Second, high-resolution contact data allow to develop individual-based computational models of disease spread that can be used to test and compare different mitigation strategies. Because of this, over the last few years a great deal of effort has been devoted to gathering data on human contact patterns in various environments [20], using methods that include diaries and surveys [14,[21][22][23][24][25][26][27][28][29], and more recently wearable sensors that detect close-range proximity [30][31][32] and face-to-face contacts [33][34][35][36][37].…”