A high-resolution human contact network for infectious disease transmission

By Marcel Salathé, Maria A. Kazandjieva, Jung Woo Lee, P. Levis, Michal Feldman, James H. Jones. Proceedings of the National Academy of Sciences of the United States of America, 2010

773 citations recorded by the source corpus.

The most frequent infectious diseases in humans—and those with the highest potential for rapid pandemic spread—are usually transmitted via droplets during close proximity interactions (CPIs). Despite the importance of this transmission route, very little is known about the dynamic patterns of CPIs. Using wireless sensor network technology, we obtained high-resolution data of CPIs during a typical day at an American high school, permitting the reconstruction of the social network relevant for infectious disease transmission. At 94% coverage, we collected 762,868 CPIs at a maximal distance of 3 m among 788 individuals. The data revealed a high-density network with typical small-world properties and a relatively homogeneous distribution of both interaction time and interaction partners among subjects. Computer simulations of the spread of an influenza-like disease on the weighted contact graph are in good agreement with absentee data during the most recent influenza season. Analysis of targeted immunization strategies suggested that contact network data are required to design strategies that are significantly more effective than random immunization. Immunization strategies based on contact network data were most effective at high vaccination coverage.

Subfields: Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/41639372b72cd9fc736b6a84fad429dc362fdd93

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Modeling and Simulation

In Statistical and Nonlinear Physics

Citations in the corpus

Digital Epidemiology (2012, background): “Models based on recorded sequences of human contacts can inform the design of containment measures and of targeted immunization strategies [27] and marks an important departure from the static representation of contact networks [28].”
Measuring Large-Scale Social Networks with High Resolution (2014, background): “showed that disease models simulated on top of proximity data obtained from a high school are in good agreement with the level of absenteeism during an influenza season [55], and emphasize that contact data is required to design effective immunization strategies.”
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees (2011, background): “New technologies are now available that allow the tracking of proximity to and interactions between individuals [28-37], greatly transforming our ability to understand and characterize social behavior [38].”
Mathematical modeling of infectious disease dynamics (2013, background): “130 Based on these measures, they constructed the transmission network and studied the potential of the disease to spread in terms of topological characteristics such as transitivity and average-path-length with respect to the duration of contact between students.”
Coupled disease–behavior dynamics on complex networks: A review (2015, methodology): “[160,161] referred to the Bluetooth technique embedded in mobile phones, which collects the proxy data of person-to-person interactions of MIT Media Laboratory in the Reality Mining program; with the help of wireless sensors, the social experiment was conducted to trace the close proximity contacts ”
Attack Robustness and Centrality of Complex Networks (2013, background): “The effect of vaccinating individuals in a contact network according to different centrality measures on the spread of an epidemic is discussed in [17].”
Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control (2011, methodology): “We performed simulations as described previously [15] with a constant vaccination rate but varying levels of assortativity (see Methods).”
Modern temporal network theory: a colloquium (2015, neutral): “See the supplementary material in references [25,45] and http://www.sociopatterns. org/ .”
Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys (2015, background): “Moreover, the time intervals between successive contacts are also broadly distributed, spanning several orders of magnitude: most intercontact durations are short, but very long durations are also observed, and no characteristic timescale emerges [15, 18, 21, 38–40].”
Statistical physics of vaccination (2016, background): “These observations clearly point out the importance of better understanding the features of close proximity contacts in order to devise efficient vaccination strategies [394, 593, 594, 713, 714].”
Contact Patterns among High School Students (2014, background): “[15], which gives the durations of Figure 14.”