Using wearable electronic sensors for assessing contacts between individuals in

various environmentsA tool for studying infectious diseases

transmission

Philippe Vanhems for the study group*Infection Control Unit, Hôpital Edouard Herriot, Lyon, France

Epidemiology and Public Health, UMR CNRS 5558 philippe.vanhems@chu-lyon.fr

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Background• Most hospital-acquired infections are transmitted by

close-contact (i.e. patients, healthcare workers, visitor or environment)

• Knowledge of contacts between individuals is therefore crucial– to study the diffusion of pathogens– to design effective control measures and target appropriate

populations

• However, little is known about the contact patterns underlying the spread of infections at hospital

• Previous studies mainly collected data based on a self-administered questionnaire, with bias

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January February

19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Room 1, Patient 1

Room 2, Patient 3

Room 2, Patient 2

Health care worker 1

Exemple of flu transmissions in one unit (2004-2005)

RFID Technology

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At low power level, the packet is received only by neighbouring tags, within a 1-2 meters radius. This can been tuned in order to reflect a

situation during which infections can be transmitted

Emissions at large power are recorded by fixed antennas which

can be used to estimate the location

of the tag4

What has already been done• See http://www.sociopatterns.org/

• Conference (Annual French Conference on Infection Control, 2009)– Stehlé J et al. (2011) Simulation of an SEIR infectious disease model on the

dynamic contact network of conference attendees. BMC Medicine. 2011 Jul 19;9:87

• School– Stehlé J et al. (2011) High-resolution measurements of face-to-face contact

patterns in a primary school. PLoS ONE 2011;6(8):e23176

• Hospitals– Isella L et al. (2011) Close encounters in a pediatric ward: measuring face-to-face

proximity and mixing patterns with wearable sensors. PLoS ONE 6(2):e17144

– Geriatrics 5

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Epidemic curves

Scenario #1

Scenario #2

Scenario #3 (influenza)

Scenario #48

Peak: d29

Results at school

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Data collected

• Persons are asked to wear the tag on their chest or waist

• Face-to-face interactions for each individual• Number of interactions between individuals• Duration of interactions between individuals• Evolution with time

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Data collection

• Monday 6 December 2010, 1PM to Friday 10 December 2010, 14PM

• Morning from 7AM to 1:30PM, Afternoon from 1:30PM to 8PM, Night from 8PM to 7AM

• Day from 7AM to 8PM, Night from 8PM to 7AM

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Implementation

Results• 50 healthcare workers/59 (85%): medical and

paramedical HCWs, administrative staff

• 29 patients/31 (94%)

• 5 consecutive days from Monday to Friday (December 2010)

• Over the study period– 14,037 contacts– Average contacts per person: 30 (6-61)– Average duration of contact per person: 46s (20s – 65min)

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0

2

4

6

8

10

12

14

16

18

2013

:05

16:3

520

:05

23:3

53:

106:

4010

:10

13:4

017

:10

20:4

00:

153:

457:

1510

:45

14:1

517

:45

21:1

50:

504:

207:

5011

:20

14:5

018

:20

21:5

01:

254:

558:

2511

:55

Num

ber o

f per

sons

Patients Medical HCWs Paramedical HCWs

Results

1414

Monday Tuesday Wednesday Thursday Friday

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Mornings % Afternoons % Days % Nights % Total

Number of

contacts

9060 64.5 4165 29.7 13206 94.1 831 5.9 14037

Cumulative

duration of

contacts

426860

(118.6 h)

65.8 185790

(51.6 h)

28.7 612900

(170.3 h)

94.5 35580

(9.9 h)

5.5 648480

(180.1 h)

Median

contact

duration

(min-max)

20

(20-2020)

20

(20-3920)

20

(20-3920)

20

(20-420)

20

(20-

3920)

Contacts : descriptive statistics

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0

100

200

300

400

500

600

700

1 4 7 1013161922 1 4 7 1013161922 1 4 7 1013161922 1 4 7 1013161922 1 4 7 1013161922

Day 1 Day 2 Day 3 Day 4 Day 5

Contacts : descriptive statistics

Results

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0

2

4

6

8

10

12

14

16

18

20

22

2413

:05

16:3

520

:05

23:3

53:

106:

4010

:10

13:4

017

:10

20:4

00:

153:

457:

1510

:45

14:1

517

:45

21:1

50:

504:

207:

5011

:20

14:5

018

:20

21:5

01:

254:

558:

2511

:55

Num

ber o

f con

tact

s

Patients-Medical HCWs Patients-Paramedical HCWs

Monday Tuesday Wednesday Thursday Friday

Results

Patients

Administrative

Paramedical

Medical

Patients

Administrative

Paramedical

Medical Cumulative number of

contacts

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Results

19

20

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

0 50 100 150 200 250 300 350

1

3

5

7

9

11

13

15

17

19

21

23

25

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Cumulative duration of contact with patients

Contacts number with patients

ncontacts.PAT

duree.PAT

Super-spreaders?

Advantages

• Detailed measurements of face-to-face interactions between individuals

• Collected data seem close to what happen actually

• Flexible and portable technology• High participation rate• Tool for communication and training

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Limits• The technology only measured interactions between

individuals– Who agree to participate and to wear a tag– Who are in the zones covered by antennas

• The technology is particularly adapted to the field of respiratory-spread infections but less likely for infections transmitted by direct or very close contact

• The limited period of time (2 to 5 days) of data collection limits the ability to draw conclusions at longer time scales– But continuous improvement of the technology

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Conclusions• Detailed measurements of interactions

– Description and possibly identification of situations at-risk of infection transmission

– Statistical inference if combined with clinical and microbiological data

– Modeling of the spread of various infectious diseases and assessing the effect of specific control measures

• Perspectives– Simulations of diseases spread using school and geriatric data

(on going)

– Larger study with more hospital wards and with microbiological samples (next winter)

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Mesures par radiofréquence des contacts en milieu hospitalier en vue de modéliser la propagation des infections nosocomiales,

application à l’infection grippale saisonnière.

Equipe Opérationnelle d’Hygiène

Unité de médecine gériatrique de court séjour K2

27 février – 9 mars 2012

Objectifs

• Décrire les contacts dans un service hospitalier en période d’épidémie grippale à l’aide de technologies RFID

• Associer ces contacts à des prélèvements virologiques permettant de connaître la présence ou non de virus respiratoires

• Appliquer à la recherche dans le domaine des infections nosocomiales

Méthode: les capteurs

Equipement, avec leur accord, des personnels soignants ayant des contacts avec les patients.

Equipements, avec leur accord ou celui des familles, des patients.

Méthode: les prélèvements

Patients : prélèvement systématique lors de l’entrée (ou début d’étude) et de la sortie (ou fin d’étude). Prélèvement supplémentaire en cas d’apparition d’un syndrome grippal.

Ecouvillonnage nasal à l’aide d’un virocult ®: prélèvements envoyés au Laboratoire de Virologie Est des Hospices Civils de Lyon.

Personnel soignant: prélèvement systématique en début et fin d’étude. Prélèvement supplémentaire en cas d’apparition de syndrome grippal.

1ers Résultats

38/44 patients badgés: 4 refus et 2 non port pour raisons médicales2 badges retirés en cours d’étude pour risque sur le

patient. 49/49 soignants badgés: 27 infirmiers et élèves infirmiers, aides soignants,

élèves aides soignants et agents de services hospitalier

15 praticiens hospitaliers, internes et externes7 autres personnels (cadre, kiné, psychologue…).

1ers résultats

137 systématiques : 84 Soignants, 53 patients

3 soignants avec syndromes

10 patients avec syndrome

150 Prélèvements

Patients Soignants Total

Virus grippal A 10 5 15

VRS 2 0 2

Picornavirus 0 2 2

Métapneumovirus 0 1 1

Pas de virus identifiés 24 41 65

Remarques: Pour des raisons médicales, 3 patients badgés n’ont pas pu être prélevés.Un patient non badgé a présenté un infection grippale A

Remerciements

Equipe GHEH-UCBL-UMR 5558: Corinne Régis, Florie Bétend, Nagham Khanafer, Etienne Pôt, Cecile Payet, Sélilah Amour, Corinne Del Signore.

Laboratoire de Virologie : Bruno Lina, Vanessa Escuret, Florence Morfin

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Philippe Vanhems, Lyon, France

Nicolas Voirin, Lyon, France

Alain Barrat, Marseille, France

Juliette Stehle, Marseille, France

Jean-François Pinton, Lyon, France

Ciro Catutto, Turin, Italy

Wouter Van den Broeck, Turin, Italy

TrueLite, Italy

BitManufaktur, Germany

References• http://www.sociopatterns.org• Cattuto C, W. Van den Broeck W, Barrat A, Colizza V, J.-F. Pinton, Vespignani A (2010)

Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS ONE 5(7):e11596

• Isella L, Romano M, Barrat A, Cattuto C, Colizza V, Van den Broeck W, Gesualdo F, Pandolfi E, Ravà L, Rizzo C, Tozzi AE (2011) Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors. PLoS ONE 6(2):e17144

• Stehlé J, Voirin N, Barrat A, Cattuto C, Colizza V, Isella L, Régis C, Pinton J-F, Khanafer N, Van den Broeck W and Vanhems P (2011) Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC Medicine, 2011

• Stehlé J, Voirin N, Barrat A, Cattuto C, Isella L, Pinton J-F, Quaggiotto M, Van den Broeck W, Régis C, Lina B and Vanhems P (2011) High-resolution measurements of face-to-face contact patterns in a primary school. PLoS ONE, 2011

• Salathé M, Kazandjieva M, Lee J W, Levis P, Feldman M W, Jones J H (2010) A High-Resolution Human Contact Network for Infectious Disease Transmission. Proc. Natl. Acad. USA 107:22020-22025

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