Mining Networks of Human Contact with Wearable Sensors

Slide from a talk given at the Data Science and Epidemiology workshop at Penn State University, http://www.salathegroup.com/dse2011/

Transcript

1. Data Science & Epidemiology Center for Infectious Disease Dynamics (CIDD) State College, October 6th, 2011 mining networks of human contact with wearable sensors Ciro Cattuto ISI Foundation http://isiosf.isi.it/~cattuto/, @ciro the experience of the SocioPatterns project aspects of dynamical network data data-driven simulation of epidemic processes open problems
2. mapping human contact networks
3. mapping human contact networks
4. mapping human contact networks
5. mapping human contact networks ★ fundamental knowledge on human mobility ★ epidemiological investigation ★ social network analysis ★ organizational investigation ★ ad-hoc / DTN / opportunistic networks
6. goal: high-resolution contact networks
7. ‣ cell phones (Onnela et al. 2007, Gonzalez et al. 2009) • localisation • mobility patterns • aggregated networks ‣ social interaction networks • Bluetooth, wifi (O’ Neill et al. 2006, Scherrer et al. 2008, Eagle and Pentland 2009) • MIT Reality Mining Project (sociometric badges) • MOSAR european project (healthcare) • Salathé group (Salathé et al. 2010) ‣ ...and a huge literature using paper-based surveys data
8. ‣ cell phones (Onnela et al. 2007, Gonzalez et al. 2009) • localisation • mobility patterns • aggregated networks ‣ social interaction networks • Bluetooth, wifi (O’ Neill et al. 2006, Scherrer et al. 2008, Eagle and Pentland 2009) • MIT Reality Mining Project (sociometric badges) • MOSAR european project (healthcare) • Salathé group (Salathé et al. 2010) ‣ ...and a huge literature using paper-based surveys data LACKING: large-scale time-resolved data on f2f proximity across a variety of contexts
9. active RFID
10. active RFID (not your usual)
11. proximity-sensing sensor network 42 17
12. proximity-sensing sensor network 42 17
13. proximity-sensing sensor network 42 17 “I am 42 and I saw 17 !”
14. proximity-sensing sensor network LAN 42 17 “I am 42 and I saw 17 !”
15. proximity-sensing sensor network LAN 42 17 “I am 42 and I saw 17 !”
16. http://www.vimeo.com/6590604 dynamical network of f2f proximity
17. http://www.vimeo.com/6590604 dynamical network of f2f proximity
18. 2008 >> 2010
19. DATE VENUE SIZE DURATION May 2008 Socio-physics workshop, Torino, IT ~65 3 days Jun 2008 ISI offices, Torino, IT ~25 3 weeks Oct 2008 ISI workshop, Torino, IT ~75 3 days Dec 2008 Chaos Comm. Congress, Berlin, DE ~600 4 days Apr-Jul 2009 Science Gallery, Dublin, IE ~30,000 3 months Jun 2009 ESWC09, Crete, GR ~180 4 days Jun 2009 SFHH, Nice, FR ~400 2 days Jul 2009 ACM HT2009, Torino, IT ~120 3 days Oct 2009 Primary school, Lyon, FR ~250 2 days Nov 2009 Bambino Gesù Hospital, Rome, IT ~250 10 days Jun 2010 ESWC10, Crete, GR ~200 4 days Apr 2010 Practice Mapping, Gijon, ES ~100 10 days Jun-Jul 2010 H-Farm, Treviso, IT ~200 6 weeks
20. >conference
21. European Semantic Web Conference 2009 + 2010 > rich user profiles: real names, co-authorship, communities of practice, interests, Facebook links, ... • 4 days • ~200 persons • 2 times
22. 25C3 conference “nothing to hide” Berlin, 27-30 december 2008 ‣ ~600 persons, 3 days
23. contact durations across contexts 102 104 Contact duration t (seconds) 10-8 10-6 10-4 10-2 100 P( ∆t) ISI 25C3 SFHH ∆ time 1 minute 30 minutes 1 hour 3 hours A
24. >museum
25. http://www.sciencegallery.com/infectious Science Gallery, Dublin 3 months ~30,000 persons
26. www.sciencemag.org SCIENCE VOL 324 22 MAY 2009 James Na Lafayette, students i to imagin subsistenc collecting The re same 30 r and orang words wit online 5 M that stude remembe the game nection w their mem The hu print of an cognition Rick O’Go the United link in the of conside areas of p SPREADING THE FLU Even a pandemic can have a silver lining. Aflood of visitors to an Irish exhibition about epidemics has become a mother lode of data on the spread of disease. On 17 April, the Science Gallery at Trinity College Dublin launched an exhibit called INFECTIOUS. To give visitors a firsthand feel for “epidemic processes,” everyone gets a radio-frequency identification tag. Tags are initially “uninfected” but can get “infected” by proximity to “infected” staff or visitors. A computer tracks everyone, mapping the spread of the infection. The timing turned out to be propitious. Soon after the opening, swine flu panic hit. “We’ve had an amazing response,” with more than 13,000 visitors so far, says gallery director Michael John Gorman. The data are flowing to computers in Italy, where epidemiologists at the Institute for Scientific Interchange Foundation in Turin are modeling epidemics. The experiment “does seem to address human-to-human contact at the most local level, which is the least well understood of organizational scales,” says Oliver Pybus, an epidemiologist at the University of Oxford in the United Kingdom. CREDITS (TOP TO BOTTOM): MICHAEL TOBLER; JON HENDERSON; PATRICK BOLGER/SCIENCE GALLERY
27. cumulative daily contact network