# 5140
Note: Fixed broken link.
A truly ingenious piece of research, published yesterday in PNAS, that uses wireless technologies to chart the opportunities an airborne virus has to spread in a closed, heavily populated environment, like a high school.
The authors wired 655 students, 73 teachers, 55 staff, and 5 others with tiny remote sensors (called motes) that detected each time any of them came within 3 meters of another individual.
The movements, contacts (including duration), and clustering of individuals were recorded every 20 seconds over the period of a single school day in January, and during that time they collected 762,868 CPIs (Close Proximity Interactions).
They then ran a series of computer simulations, assigning each student (1 student at a time) as an index case with an infectious respiratory virus, and using thousands of computer runs, determined whether or not secondary transmission would occur.
Although no secondary transmission occurs in 2/3rds of the simulation runs - were a virus circulating in the community - multiple introductions would be expected, raising the odds of ongoing transmission.
The researchers also looked at the effects on transmission due to different vaccination strategies (random, students, teachers).
There’s a lot in this open access paper, which is admittedly heavy on math and statistics. Luckily we’ve a press release, a short audio podcast, and the abstract to give us the highlights.
First, the study and abstract (slightly reparagraphed for readability).
A high-resolution human contact network for infectious disease transmission
10.1073/pnas.1009094108
Marcel Salathé, Maria Kazandjieva, Jung Woo Lee, Philip Levis, Marcus W. Feldman, and James H. Jones
Abstract
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.
You can listen to a brief interview with one of the authors (Marcel Salathé) HERE.
And a press release, from the National Science Foundation, provides the basics.
Human networking theory gives picture of infectious disease spread
High school students' interactions provide new look at disease transmission
It's colds and flu season, and as any parent knows, colds and flu spread like wildfire, especially through schools.
New research using human-networking theory may give a clearer picture of just how, exactly, infectious diseases such as the common cold, influenza, whooping cough and SARS can spread through a closed group of people, and even through populations at large.
With the help of 788 volunteers at a high school, Marcel Salathé, a biologist at Penn State University, developed a new technique to count the number of possible disease-spreading events that occur in a typical day.
This results are published in this week's issue of the journal Proceedings of the National Academy of Sciences.
The research was funded by the National Science Foundation (NSF) and the National Institutes of Health (NIH).
My thanks to Carol@SC and Jane on the Flu Wiki for the head’s up on these links.
