From sensor data to targeted interventions. Presented by Ciro Cattuto, Research Director, ISI Foundation, Italy, at HINZ 2014, 12 November 2014, 10am, Plenary Room

1. High-Resolution Social Networks for Health
from sensor data to targeted interventions
Ciro Cattuto
ISI Foundation
HiNZ Conference 2014
Auckland, 12 November 2014

2. ‣ basic and applied research
‣ 30+ years of history
‣ Turin, Italy and New York, USA
‣ international network
‣ supported by:
• bank foundations
• EU research grants
• industrial partnerships
‣ focus on
• complex systems science
• network science
• mathematical modeling
• data-driven decision making
www.isi.it

3. Data Driven
Approach
Theory &
Models
Mathematics &
Foundation of
Complex
Data Science Systems
Computational
Social Science
Quantum
Science &
Complexity
Collective Phenomena in Physics
& Materials Science
Complexity
Science
Computational Epidemiology
& Public Health
Citizen Science
& Smart Cities
ISI Foundation

4. why now ?
‣ the digital image of the world is tracking the world
more and more closely
• this allows us to use computation to extract patterns
and establish causal inferences using tools from data
mining, machine learning, statistics
• mathematical modeling and forecast now happen on a
data-rich landscape (e.g., behavioral data, social network
data) and are fed by data streams from multiple sources
• we can assess our models against reality at
unprecedented speed and scale, and feed back to models

5. ✓ large number of components
✓ interactions between components
✓ multi-scale hierarchical structures
✓ coupling between scales
✓ self-organization (no blueprint)
✓ emergent properties
✓ “complex” is more than “complicated”
A. Koblin
P. Butler
complex systems

6. not complex

7. complex
http://www.uh.edu/engines/epi2553.htm
Cathedral Termite
(Nasutitermes triodiae)

8. complex systems
✓ large number of components
✓ interactions between components
✓ multi-scale hierarchical structures
✓ coupling between scales
✓ self-organization (no blueprint)
✓ emergent properties
✓ “complex” is more than “complicated”
A. Koblin
P. Butler
★ the end of linear thinking
★ systemic view of risk
★ the problem of causal inference

9. www.gov.uk/government/publications/reducing-obesity-obesity-system-map

10. data to model to decision

11. data to model to decision
data

12. data to model to decision
math. modeling,
complex systems,
network science
data mining,
machine learning,
natural language
processing
data

13. data to model to decision
human-machine compositionality
math. modeling,
complex systems,
network science
data mining,
machine learning,
natural language
processing
data

14. data to model to decision
decision and policy making
human-machine compositionality
math. modeling,
complex systems,
network science
data mining,
machine learning,
natural language
processing
data

16. high-resolution social networks

17. high-resolution social networks
‣ human mobility modeling
‣ location-aware services
‣ organizational science
‣ social science & health
‣ infectious disease dynamics

18. hospital acquired infections
931M £ / year
5.7B $ / year
137M NZ$ / year
J Hosp Infect. 2001 Mar;47(3):198-209
http://www.cdc.gov/hai/pdfs/hai/scott_costpaper.pdf
Infect Control Hosp Epidemiol. 2003 Mar;24(3):214-23

19. manually

20. automatically

21. proximity sensors

22. • RTLS
• active RFID

23. • RTLS
• active RFID
• beacons

24. • RTLS
• active RFID
• beacons
• direct
proximity
sensing

25. • direct
proximity
sensing

26. • direct
proximity
sensing

27. • direct
proximity
sensing

28. • direct
proximity
sensing

29. • direct
proximity
sensing
recorded proximity data

30. high-resolution social network
http://www.vimeo.com/6590604

31. high-resolution social network
http://www.vimeo.com/6590604

32. SocioPatterns.org
5 years, 25+ deployments, 10 countries, 50,000+ persons
• Mongan Institute for Health Policy, Boston
• US Army Medical Component of the Armed Forces, Bangkok
• School of Public Health of the University of Hong Kong
• KEMRI Wellcome Trust, Kenya
• London School for Hygiene and Tropical Medicine, London
• Public Health England, London
• Saw Swee Hock School of Public Health, Singapore

33. sensor data
+
analytics

34. hospitals

36. Rome, Italy
Bambino Gesù
children hospital

37. doctors
nurses
auxiliaries
children
parents
A
N D
C
P

38. role-based contact matrices
number of contacts sn
63.0
0.3
6.5
0.1
0.5
7.4
2.4
0.4
15.9
2.4
23.0
0.8
1.1
0.9
2.0
0.1
2.3
0.6
1.9
12.8
A D N P C
A
D
N
P
C
0.4
0.5
0.9
15.0
0.9
A
N D
number of distinct contacts sp
1.1
0.3
1.0
0.1
0.4
0.9
0.8
0.3
1.9
0.8
2.1
0.4
0.8
0.3
0.6
0.1
1.1
0.4
0.9
0.3
A D N P C
A
D
N
P
C
0.3
0.4
0.5
0.3
0.1
cumulative time in contact st (min)
max
min
38.5
0.2
3.1
0.1
0.2
3.8
1.2
0.2
7.8
1.0
12.9
0.4
0.4
0.5
0.9
0.0
1.0
0.2
1.0
11.3
A D N P C
A
D
N
P
C
0.2
0.3
0.5
15.3
0.3
C
P
A B C
L. Isella et al., PLoS ONE 6(2), e17144 (2011)

39. mining associations between contacts & hospital acquired infections
doctors children
auxiiaries parents
nurses
statistics & machine learning

40. acute care geriatric unit (Lyon, 2012)
!
Work or hospitalization period
Contagious period
Cumulative contacts duration < 60s
Cumulative contacts duration ≥ 60s and < 120s
Cumulative contacts duration ≥ 120s
● Symptoms onset
+ Influenza positive swab
- Influenza negative swab
!
• proof-of-concept observational study
• 37 patients, 32 nurses, 15 doctors
• 12 days of high-res contact data
• nasopharyngeal swabs
• PCR-confirmed influenza A & B infections
• culture-based subtyping and phylogenetics
D1** D2 D3
D4
D5 D6 D7 D8 D9 D10 D11 D12
+ +
+ -
+ ●
+ +
+ +
+ +
+ -
- + ● +
- + ● +
● + +
● + +
● + -
- - ● +
Symptoms onset March 2
600 (PAT) * Symptoms onset March 3
683 (PAT) ● + +
Symptoms onset March 6 (reported by the patient) + isolation
- ● +
Culture
result
nd
neg
pos †
neg 647 (PAT)
nd
nd
nd
neg
pos †
nd
neg
neg
neg
nd
pos †
RFID tag
number (group)
657 (PAT) *
Comments
602 (PAT) Asymptomatic
633 (MED) Symptoms onset February 26
640 (MED) Symptoms onset February 27
Symptoms onset February 26
609 (PAT) Symptoms onset February 24 + isolation
626 (NUR) Back from sick leave February 28 after a previous ILI episode
663 (NUR) Back from sick leave March 1 after a previous ILI episode
612 (PAT) * Symptoms onset March 2
675 (PAT) Symptoms onset March 3 (reported by the patient)
677 (PAT) Symptoms onset March 3 (reported by the patient)
678 (PAT) Symptoms onset March 2 (reported by the patient) + isolation
644 (NUR) * Symptoms onset March 7

41. acute care geriatric unit (Lyon, 2012)
N. Voirin et al.
Combining high-resolution
contact data with virological
data to investigate influenza
transmission in a tertiary care hospital
Infection Control and Hospital
Epidemiology, in press (2014)

42. primary schools

43. primary
school

44. primary
school
Lyon, France
primary school
231 students
10 teachers

46. Hong Kong
primary school
900 students
65 teachers

47. class-based contact matrix
min

48. groups and activity schedule

49. validation: sensors vs direct observation
collaboration with Gabriel Leung’s group at the University of Hong Kong
Figure'1' Figure'2'
• record positions of students in a
classroom for 30 minutes
• annotate spatio-temporal contact
patterns from the video (positions
and orientations of subjects)

50. K-8 school
San Francisco, 2012
• ~50 6th graders (90.9% participation)
• face-to-face interactions during lunch
breaks + physical activity + self-reported
info on health, eating and physical exercise
• longitudinal study: 3 periods of 3
consecutive days at 1-month intervals
• goal: micro-changes in socialization patterns
in relation to depression and self-esteem,
without reliance on network self-report

52. network indicators vs mental health
Pearson correlation coefficients

53. study results
• social interaction is associated with
mental health status in early adolescence
• girls with depressive symptoms are more
socially inhibited than boys with
symptoms.
• girls high in self-esteem tend towards
greater network social integration
• social influence does not shape self-esteem
or depression at this age
M.C. Pachucki, E.J. Ozer, A. Barrat, C. Cattuto
Social Science & Medicine, in press (2014)

54. data-driven design of
targeted interventions

55. intervention design by means of data-driven simulation
doctors children
auxiiaries parents
nurses
simulation

56. epidemic models & micro-interventions
interventions based on
observed cases +
contact matrix
epidemic model
simulated
using high-resolution
contact network

57. epidemic models & micro-interventions
interventions based on
observed cases +
contact matrix
epidemic model
simulated
using high-resolution
contact network

58. policy 1: school closure
• we close the school when we observe a total number
of symptomatic cases in excess of a fixed threshold
• we close the school for a fixed time interval (24, 48, 72 hours)
( flu-like + 1/3 asymptomatic subjects + off-school infection probability )

59. policy 2: targeted class closure
• we close a class when we observe there a number of
symptomatic cases in excess of a fixed threshold (1, 2, 3, ...)
• we close the class for a fixed time interval (24, 48, 72 hours)
V. Gemmetto et al., arxiv.org/abs/1408.7038

60. high-resolution social networks
• new health-related behavioural signals
• support for outbreak investigation
• protocol compliance analytics
• data-driven tuning of protocols
• performance metrics during training exercises
• evidence-based approach to intervention design

61. organized by Boston Children’s Hospital, Healthmap, ISI Foundation,
Skoll Global Threats Fund, Northeastern University
co-located with WWW2015 and ACM Digital Health 2015

62. thank you
@ciro
www.cirocattuto.info