About the Project
Many infectious diseases spread via droplets during close proximity interactions between infected and healthy individuals. For example, every year about 5% to 20% of people in the United States get sick with the flu. Understanding how individuals interact with each other is key to studying the spread of such diseases.
In January 2010, we set out to collect data on all close proximity interactions in a closed network - a U.S. high school. We recruited over 700 people - students, teachers, and staff - and asked them to wear wireless TelosB motes around their necks for a day. Every 20 seconds, the motes broadcast 'Hello' messages, with nearby nodes recording what they heard. At the end of the school day, we collected enough data to reconstruct the real-life social network of the entire school.
The publications below give details on both the technical and epidemiology aspects of our work. You can also explore the code used to collect the data, as well as the dataset itself. Feel free to contact Maria (technical q's) and Marcel (epidemilogy q's). Welcome, and enjoy!
- Marcel Salathé, Maria Kazandjieva, Jung Woo Lee, Philip Levis, Marcus W. Feldman and James H. Jones.
A High-Resolution Human Contact Network for Infectious Disease Transmission.
In Proceedings of the National Academy of Sciences (PNAS), December 13, 2010.
[WWW] [SUPPL] [PDF]
- Maria Kazandjieva, Jung Woo Lee, Marcel Salathe, Marcus W. Feldman, James H. Jones, and Philip Levis.
Experiences in Measuring a Human Contact Network for Epidemiology Research.
Proceedings of the ACM Workshop on Hot Topics in Embedded Networked Sensors (HotEmNets),
June 28-29, 2010.Killarney, Ireland
- flu-data.zip (19.8 MB) contains the dataset from the publications above
- flu-code.zip (205 KB): code used to collect proximity beacons and retrieve them from the motes
- TinyOS Tutorial (just in case)
In the Media
- Stanford covered our work in a December'10 article
- A few other online news sources have mentioned our work as well. See a list here