Using wearable electronic sensors for assessing contacts between individuals in various environments A 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

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

Exemple of flu transmissions in one unit (2004-2005) 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 2 Room 2, Patient 3 Health care worker 1

RFID Technology 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 tag 4 4

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

Scenario #3 (influenza) Epidemic curves Scenario #3 (influenza) Scenario #1 Scenario #2 Peak: d29 Scenario #4 8

Results at school

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

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

Implementation 12

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)

Results Monday Tuesday Wednesday Thursday Friday 14

Contacts : descriptive statistics 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) (20-3920) (20-420)

Contacts : descriptive statistics

Results Monday Tuesday Wednesday Thursday Friday 17

Results Patients Administrative Paramedical Medical Patients Medical Cumulative number of contacts Patients Medical Paramedical Administrative

Results

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

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

Conclusions Detailed measurements of interactions Perspectives 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)

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 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. 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.

1ers Résultats 38/44 patients badgés: 4 refus et 2 non port pour raisons médicales 2 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 externes 7 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 Picornavirus Métapneumovirus 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

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